Способ и устройство для мониторинга состояния здоровья растений

Группа изобретений относится к области интеллектуального сельского хозяйства. Техническим результатом является увеличение контролируемой области посадок и повышение скорости обследования растений. Группа изобретений представляет собой способ контроля состояния здоровья растений, включающий в себя: выполнение первого определения в соответствии с информацией о состоянии здоровья растений; когда результатом определения является то, что существует риск плохого состояния здоровья растений, выполнение второго определения в соответствии со второй информацией о состоянии здоровья растений в ориентации, где находится по меньшей мере одно растение с риском плохого состояния здоровья растений, чтобы точно получить информацию о состоянии здоровья растений, и устройство, реализующее этот способ для мониторинга состояния здоровья растений. 2 н. и 13 з.п. ф-лы, 8 ил.

ЛЕДНИКОВОЕ ГЕОМОРФОЛОГИЧЕСКОЕ КАРТОГРАФИРОВАНИЕ

... 1. Способ картографирования ледниковой геоморфологии, содержащий этапы, на которых:принимают изображение исследуемого района, полученное с помощью спутника;принимают цифровую модель возвышений исследуемого района;идентифицируют равнины и гряды на цифровой модели возвышений;идентифицируют болота и леса на изображении, полученном с помощью спутника; иформируют гляциологическую карту, имеющую ледниковые признаки, на основе идентифицированных равнин, гряд, болот и леса.2. Способ по п.1, в котором идентификацию равнин и гряд на цифровой модели возвышений используют для оценки скоростей сейсмических поверхностных волн или для оценки шума сейсмических поверхностных волн, а также для затухания сейсмических волн.3. Способ по п.1, в котором гляциологическую карту формируют корреляцией равнин на цифровой модели возвышений с болотами на изображении, полученном с помощью спутника; и корреляцией гряд на цифровой модели возвышений с лесом на изображении, полученном с помощью спутника.4. Способ по п.1, ...

СИСТЕМЫ И СПОСОБЫ ЗАХВАТА ИЗОБРАЖЕНИЙ В УСТАНОВКЕ КОНВЕЙЕРНОГО ТИПА ДЛЯ ВЫРАЩИВАНИЯ РАСТЕНИЙ

Tiefgreifendes maschinelles Lernen zur Vorhersage und Verhinderung ungünstiger Zustände bei baulichen Anlagen

Die vorliegende Offenbarung schafft Systeme und Verfahren, die maschinell erlernte Modelle, wie z. B. tiefe neuronale Netze, verwenden, um ungünstige Zustände bei baulichen Anlagen vorherzusagen und zu verhindern. Ein beispielhaftes Verfahren enthält das Erhalten von Daten, die mehrere Bilder beschreiben, die wenigstens einen Abschnitt eines geographischen Bereichs, der eine erste bauliche Anlage enthält, darstellen. Die mehreren Bilder enthalten wenigstens ein erstes Bild, das zu einem ersten Zeitpunkt aufgenommen worden ist, und ein zweites Bild, das zu einem zweiten Zeitpunkt, der von dem ersten Zeitpunkt verschieden ist, aufgenommen worden ist. Das Verfahren enthält das Eingeben von Daten, die wenigstens das erste Bild, den ersten Zeitpunkt, das zweite Bild und den zweiten Zeitpunkt beschreiben, in ein Zustandsvorhersagemodell. Das Verfahren enthält das Empfangen wenigstens einer Vorhersage hinsichtlich des Auftretens eines ungünstigen Zustands bei der ersten baulichen Anlage während ...

Method and system for collecting agricultural image data

Tool for counting and sizing plants in a field

The application relates to agricultural technology and artificial intelligence, to counting and sizing plants in a field. One aspect relates to a method for analysing plants in an area of interest that generally includes providing at least one aerial image of the area of interest, performing object detection, segmentation and instantiation using an object-mask-predicting region convolutional neural network, Mask R-CNN, wherein the Mask R-CNN is trained to detect a selected vegetable. The numbers and sizes of objects detected is determined by dividing the area of interest into multiple cells, calculating the average object size per cell and displaying results in the form of a map of the area of interest with colour or scale for each cell corresponding to the average size of object in the cell.

Disease recognition method of winter jujube based on deep convolutional neural network and disease image

The winter jujube disease identification method is proposed based on deep convolutional neural network and disease image. Firstly convert the original winter jujube disease RGB color image collected by IOT into a YUV color model, and then preprocess it, then obtain the disease rectangular area of interest, segment the spot image by K-means clustering algorithm to obtain YUV color lesion image. Construct a three-channel hierarchical convolutional neural network model, use the training data to train the model, and finally input the winter jujube disease image into the trained model to identify the disease category. The invention can be applied to the greenhouse winter jujube disease based on IOT. In the monitoring system, higher disease identification results can be obtained. -1/3 S Color components Hierarchical Deep Convolutional Neural Network Color Hierarchical Deep Classif- Output Disease-- * Color components Convolutional Neural Network ication recognition image Flayer result S Color ...

A NON-DESTRUCTIVE TESTING METHOD FOR AGRICULTURAL PRODUCT QUALITY INDICATORS

The present invention discloses a method for non-destructive detection of quality indicators of agricultural products, which comprises the following steps: Step 1: Build a machine vision 5 detection platform, and then collect RGB images of an agricultural product in a vision sensor in all directions; Step 2: Use the images collected in Step 1 to image segmentation, and then divide the region of interest for the agricultural product, and extract the contours of the agricultural product; Step 3: Use the contours of the agricultural products extracted in Step 2, and use the shape invariant feature extraction operator to extract the shape feature vectors to overcome the 0 problems of different sizes, positions and orientations of agricultural products. Step 4: Use the sample images of the agricultural product with different shapes collected in step 1, and use the feature vectors extracted in step 2 and step 3 to train a support vector machine to determine the optimal parameters and detection ...

CNN AND TRANSFER LEARNING BASED DISEASE INTELLIGENT IDENTIFICATION METHOD AND SYSTEM

Abstract The present invention provides a CNN and transfer learning based disease intelligent identification method and system, which could lower the interference of picture background, achieve a high identification accuracy in the case of a limited quantity of samples and support 5 multi-classification of training samples, and is thus high in operating efficiency. A disease image intelligent identification method comprises the following steps of: image preprocessing: normalizing the sizes of images, and quickly positioning a region of sign of disease using Faster-RCNN to exclude background interferences; image feature extraction: extracting image features using a triplet similarity measurement model, and then using SIFT feature as a 0 complementary feature to perform weight fusion; and disease classification and identification: learning a first image feature of normal plant images using a deep convolutional neural network, learning a second image feature of diseased plant images through ...

Disease recognition from images having a large field of view

A computer system and related computer-implemented methods for recognizing crop diseases from large FoV images are disclosed. In some embodiments, the computer system is configured to initially build a first digital model in memory for identifying a region capturing a leaf, and a second model for identifying a region capturing a leaf infected with a disease. Given a large FoV image, under program control, the system is programmed to then automatically identify candidate regions that might capture single leaves from the large FoV image using the first model. The system is programmed to further determine whether the candidate regions capture symptoms of a crop disease on single leaves using on the second model.

Work layer imaging and analysis for implement monitoring, control and operator feedback

A soil imaging system having a work layer sensor disposed on an agricultural implement to generate an electromagnetic field through a soil area of interest as the agricultural implement traverses a field. A monitor in communication with the work layer sensor is adapted to generate a work layer image of the soil layer of interest based on the generated electromagnetic field. The work layer sensor may also generate a reference image by generating an electromagnetic field through undisturbed soil. The monitor may compare at least one characteristic of the reference image with at least one characteristic of the work layer image to generate a characterized image of the work layer of interest. The monitor may display operator feedback and may effect operational control of the agricultural implement based on the characterized image.

Device and method for detecting a plant against a background

A device for detecting a plant against a background includes a means (10) for providing a plurality of different photographs of the plant leaf against the background, the photographs differing in that image points of the different photographs which relate to the same location of the plant leaf are illuminated with different levels of brightness, a means (12) for selecting such image points, from the different photographs, whose levels of brightness are within a predetermined range, an image point of a first photograph being selected for a first location of the plant leaf, and an image point of a second, different photograph being selected for a different location of the plant leaf so as to obtain a representation of the plant leaf against the background, said representation being composed of and/or merged from different photographs, and a means (14) for segmenting the composite photograph so as to obtain a segment representation comprising the plant leaf without the background or the background ...

Device and method for detecting a plant against a background

A device for detecting a plant against a background includes a means (10) for providing a plurality of different photographs of the plant leaf against the background, the photographs differing in that image points of the different photographs which relate to the same location of the plant leaf are illuminated with different levels of brightness, a means (12) for selecting such image points, from the different photographs, whose levels of brightness are within a predetermined range, an image point of a first photograph being selected for a first location of the plant leaf, and an image point of a second, different photograph being selected for a different location of the plant leaf so as to obtain a representation of the plant leaf against the background, said representation being composed of and/or merged from different photographs, and a means (14) for segmenting the composite photograph so as to obtain a segment representation comprising the plant leaf without the background or the background ...

SYSTEM AND METHOD FOR INTEGRATED SURVEILLANCE AND COMMUNICATION INTO LIGHTING EQUIPMENT

A system including a first luminaire housing, a second luminaire housing, and a controller. The first luminaire housing includes a first light source configured to provide illumination to a first location, and a first I/O device configured to sense a first data set corresponding the first location. The second luminaire housing is separate from the first luminaire housing and includes a second light source configured to provide illumination to a second location, and a second I/O device configured to sense a second data set corresponding to the second location. The controller is configured to receive a portion of the first data set and a portion of the second data set, combine the portion of the first data set and the portion of the second data set to form a third data set corresponding to the first location and second location, and output the third data set.

CROP TYPE CLASSIFICATION IN IMAGES

In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations.

IMAGE-BASED IRRIGATION RECOMMENDATIONS

Techniques for providing improvements in agricultural science by optimizing irrigation treatment placements for testing are provided, including analyzing a plurality of digital images of a field to determine vegetation density changes in a sector of the field. The techniques proceed by comparing a distribution of pixel characteristics in the digital images for each field sector to determine sectors in which minimal density deviations are present. Instructions for irrigation placements and testing may be displayed or modified based on the results of the sector determinations.

CROP YIELD PREDICTION AT FIELD-LEVEL AND PIXEL-LEVEL

Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.

HANDHELD DEVICE AND METHOD FOR DETERMINING A PLANT STATUS

The invention relates to a handheld device and method for determining a status of one or more plants. The device includes a digital color camera arranged for obtaining a color image of plants within a field of view, a light source arranged for providing broadband illumination to the plants within the field of view, and a processing unit arranged for controlling the camera and the light source for obtaining a first image of the plants while the light source illuminates the plants with broadband illumination, and obtaining a second image of the plants while the light source does not illuminate the plants.

APPARATUS, SYSTEM AND METHODS FOR IMPROVED VERTICAL FARMING

The present disclosure is directed to improved vertical farming using autonomous systems and methods for growing edible plants, using improved stacking and shelving units configured to allow for gravity-based irrigation, gravity-based loading and unloading, along with a system for autonomous rotation, incorporating novel plant-growing pallets, while being photographed and recorded by camera systems incorporating three dimensional/multispectral cameras, with the images and data recorded automatically sent to a database for processing and for gauging plant health, pest and/or disease issues, and plant life cycle. The present disclosure is also directed to novel harvesting methods, novel modular lighting, novel light intensity management systems, real time vision analysis that allows for the dynamic adjustment and optimization of the plant growing environment, and a novel rack structure system that allows for simplified building and enlarging of vertical farming rack systems.

A WEED CONTROL APPARATUS

The present invention relates to a weed control apparatus (10) for a vehicle. It is described to provide (210) a processing unit with at least one image of an environment. The processing unit analyses (220) the at least one image to activate at least one chemical spray unit. A liquid weed control chemical is atomized and charged (230) by the at least one chemical spray unit. The at least one chemical spray unit has at least one part configured to be held at high voltage with respect to zero volts potential. The at least one high voltage power supply and the at least one chemical spray unit are configured to hold the at least one part of the at least one chemical spray unit at one or a number of high voltages with respect to zero volts potential, such that the atomized liquid weed control chemical is electrically charged.

SYSTEMS AND METHODS FOR AGRICULTURAL MONITORING

An agricultural monitoring system, the agricultural monitoring system comprising: an imaging sensor, configured and operable to acquire image data at submillimetric image resolution of parts of an agricultural area in which crops grow, when the imaging sensor is airborne; a communication module, configured and operable to transmit to an external system image data content which is based on the image data acquired by the airborne imaging sensor; and a connector operable to connect the imaging sensor and the communication module to an airborne platform.

SYSTEM FOR MAPPING AND IDENTIFICATION OF PLANTS USING DIGITAL IMAGE PROCESSING AND ROUTE GENERATION

The present invention relates to a system and method for mapping of plants. Homogenous and heterogeneous flora areas called clusters are identified in remote sensing images and routes to the plant clusters are generated. The plants are classified using morphological data from foliar images of plants present in the clusters.

Methode zur Diagnose von Stickstoff, Kalium und Magnesiummangel in Pflanzen basierend auf den Verteilungseigenschaften von Blattchlorophyll auf der Blattoberfläche.

Die Erfindung betrifft ein Verfahren zur Diagnose von Stickstoff, Kalium und Magnesiummangel in Pflanzen basierend auf den Verteilungseigenschaften von Blattchlorophyll auf der Blattoberfläche, wobei zuerst die Blattfläche des zu testenden Blattes in mehrere kleine Bereiche unterteilt wird, die regionalen Verteilungseigenschaften des Chlorophylls extrahiert werden de durchschnittliche. Chlorophyllgehalt, die Varianz des Chlorophyllgehalts, de maximale Chlorophyllgehalt und de minimale Chlorophyllgehalt welche alle Bildpunkten in dem kleinen Bereich der Blattchlorophyllblattverteilungsdiagramme entsprechen mithilfe der hyperspektralen Bildtechnologie extrahiert werden; auf dieser Grundlage wird das Diagnosemodell für N-, K- und Mg-Mangel erstellt, gemäß dem Modell kann N-, K- und Mg-Mangel in den zu testenden Blättern diagnostiziert werden. Die Einschränkung, dass das Verfahren zur Diagnose basierend auf dem Chlorophyllgehalt den Mangel an Stickstoff-, Kalium- und Magnesium von Gurkenblättern ...

METHOD OF PRODUCING AND ANALYSIS OF AERIAL PHOTOGRAPHS

forest land change detection method based on early-stage forest land vector data

METHOD AND SYSTEM FOR MANAGING AN AGRICULTURAL PLOT

METHOD OF MEASURING THE DIRECTION OF LINEAR ELEMENTS AND DISPOSED PARALLEL TO ONE ANOTHER

Le procédé de mesure de la direction de motifs d'éléments linéaires et parallèles entre eux comprend l'exécution, au moins trois fois à la suite, de la série d'étapes suivantes - délimiter une portion d'une image comprenant les motifs, - appliquer au moins un filtre à la portion de l'image, - déterminer une valeur d'un paramètre de la portion filtrée, constituant une estimation d'un alignement entre les différents motifs, et - enregistrer la valeur déterminée, puis, après cette exécution, comparer les valeurs enregistrées et déterminer une valeur maximale du paramètre.

수경 재배 장치

... 수경 재배 장치(100)는, 식물(1)의 지상부(1B)가 성장하는 지상 공간으로부터 분리되도록, 식물(1)의 지하부(1A)가 성장하는 지하 공간을 내포하는 재배실 지하부(67)와, 지하 공간의 분위기의 온도를 조정하는 지하 온도 조정부(2478)와, 지하 온도 조정부(2478)를 제어하는 제어부(50)를 구비하고, 제어부(50)는, 식물(1)의 재배 시기가 지상부(1B)의 잎이 시든 휴면기인지의 여부를 판정하는 휴면 판정부와, 휴면 판정부에 의해 식물(1)의 재배 시기가 식물(1)의 지상부(1B)의 잎이 시든 휴면기라고 판정된 경우에, 식물(1)의 맹아가 촉진되도록, 지하 온도 조정부(2478)에 지하 공간 분위기의 온도를 조정시키는 온도 제어부를 포함하고 있다.

Applaratus for Monitoring Crop Growth through Multispectral Image Histogram Pattern Analysis of Plot Unit

sistemas e métodos para captura de imagem e análise de campos agrícolas

método e sistemas para analisar um campo

METHOD FOR AUTOMATIC OBTAINING OF AGRONOMIC AND ENVIRONMENTAL INDICATORS FROM TREE PLANTATIONS BY REMOTE DETECTION

This method quantitatively characterises tree plantations using high resolution spatial remote detection and processing of the related images, wherein information is provided on each tree and on the plantation as a whole. Thus, the method provides individualised information including the geographical baricentric coordinates, surface and potential productivity on each tree, also characterises tree plantations as a whole, calculating among other parameters the total number of trees, their surface and overall potential productivity, and indicators of range of other land uses that may be defined, such as plant cover and bare land. Therefore the method is useful in the agricultural and environmental sectors.

MONITORING SYSTEM FOR CONTROLLING WIRELESSLY CONTROLLED PLANTER

Disclosed is a monitoring system for controlling a wirelessly controlled planter (M), which is connected to a wireless controller (C) that is configured with at least a plurality of buttons and joysticks for remote control. The monitoring system includes: a detecting module (S) including a camera (S) and a GPS (S); a communication module (S); a calculating module (S); a safety module (S); and a display module (S). Accordingly, images and positions to secure a visual field for controlling and detected information of the planter are output to the controller, so that an operator safely controls the planter at any place without watching the planter in a planting field and can maintain the planter easily. 1. A monitoring system for controlling a wirelessly controlled planter (M) , which is remotely controlled in conjunction with a wireless controller (C) that is configured with at least a plurality of buttons and joysticks , the monitoring system comprising:{'b': 10', '11', '12', '11', '12, 'a detecting module (S) including a camera (S) and a GPS (S), at least one camera (S) being attached to the planter (M) to capture an image of a surrounding and a planting area and the GPS (S) measuring a location of the planter (M) and a moving distance thereof in real time;'}{'b': 20', '10, 'a communication module (S) provided in each of the controller (C) and the planter (M), sending and receiving a detected signal of the detecting module (S) and a travelling signal of the planter (M);'}{'b': '30', 'a calculating module (S) coding the detected signal sent to the controller (C) into readable sensing information and decoding manipulating information of an operator into the travelling signal that is perceived by the planter (M);'}{'b': '40', 'a safety module (S) analyzing travelling state by comparing the sensing information to reference information set by the operator, and outputting a warning sound and a warning light depending on the travelling state; and'}{'b': 50', '51', '55, 'a ...

Crop grading via deep learning

Methods and systems for crop grading and crop management. One or more images of crops are obtained and one or more crop related features are at least one of identified or extracted from the one or more images. A crop health status is determined based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis. One or more actions are at least one of recommended, triggered, and performed.

Plant provenance and data products from computer object recognition driven tracking

This disclosure sets forth systems and techniques that trace plant provenance and determine a likelihood of plant spoilage within a plant growth operation. Particularly, a plant health monitoring system may monitor and record plant growth data of one or more individual plants during a plant life cycle from germination through to harvest. The plant growth data may include data relating to environmental conditions that surround the individual plants, and plant-specific data drawn from time-lapse images captured during the plant life cycle. A plant grower may use the time-lapse image data to identify changes in plant structure that indicate the degree of health of an individual plant, or the encompassing batch of plants. Further, a plant grower may use the plant growth data to generate one or more financial instruments that may be distributed to one or more financial institutions as support for securing capital to fund the plant growth operation.

Three-dimensional map display system

A map database stores three-dimensional polygons of features, as well as water system polygons such as sea and lake and ground surface polygons. The map database stores map data in multiple levels having different levels of details, such as levels LVa to LVc. A procedure of displaying a three-dimensional map offsets the water systems relative to the ground surfaces, draws a map of a distant view area distant away from the viewpoint using map data at a low level of details, subsequently clears a depth buffer and newly draws a map of a close view area close to the viewpoint using map data at a high level of details. The offset is set to increase in the distant view area and decrease in the close view area. Increasing the offset in the distant view area avoids the occurrence of Z-fighting in the distant area from the viewpoint.

Systems, devices, and methods for in-field diagnosis of growth stage and crop yield estimation in a plant area

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

MONITOR AND CONTROL SYSTEM FOR A HARVESTER

A harvester including a frame supported by a drive assembly for movement along a support surface, a head unit coupled to the harvester and configured to selectively harvest crop material, a camera coupled to the frame and configured to generate one or more images of a field of view, and a controller in operable communication with the camera and the head unit, where the controller is configured to determine one or more crop attributes based at least in part on the one or more images produced by the camera.

Generating real-time sensor maps from videos and in-ground sensor data

An apparatus for generating precision maps of an area is disclosed. The apparatus receives sensor data, where the sensor data includes sensor readings each indicating a level of a parameter in one of a plurality of first portions of an area, and video data representing an aerial view of the area. The sensor data may be received from sensors that are each deployed in one of the first portions of the area. The video data may be received from an aerial vehicle. An orthomosaic may be generated from the video data, and the orthomosaic and the sensor data used to generate a predication model. The prediction model may then be used to extrapolate the sensor data to determine a level of the parameter in each of a plurality of second portions of the area. A precision map of the area may be generated using the extrapolated sensor readings.

Method and system for the management of an agricultural plot

The present invention relates to a decision support method for the management of an orchard of fruit trees, the method comprising the following steps: Receiving (10) a plurality of digital images acquired in color, Then for each current image of the plurality of images: Converting (20) the current digital image acquired, into a converted HSL image coded in Hue Saturation and Lightness components, Filtering (30) the Hue Saturation and Lightness components, Combining (40) the filtered Hue Saturation and Lightness planes (5, 6, 7) to obtain a Combined plane (12), Calculating (50) a Work plane (13) from the Combined plane (12), Counting (60) the objects of interest in the Work plane (13), Determining (70) a blossoming level for each current image as a function of the number of objects of interest counted for said current image.

Apparatus for plant management

The present invention relates to an apparatus for plant management. It is described to provide (210) a processing unit with at least one image of a field. The processing unit analyses (220) the at least one image to determine information relating to a plant that is present. The processing unit determines (230) if the plant is to be controlled or is not to be controlled by a plant control technology based on the information relating to the plant. An output unit outputs (240) information that is useable to activate at least one plant control technology if the determination is made that the plant is to be controlled by the plant control technology.

TRENCH PROFILE DETERMINATION BY MOTION

A method for identifying geometric parameters of a trench during a planting process. The method includes, providing image data of the trench from a camera to a computing device, the image data including more than one image, identifying at least one artifact in a first image and a second image of the image data, determining camera displacement between the first image and the second image, and applying the camera displacement and positioning of the at least one artifact to identify a geometric location of the at least one artifact identified in the first image and the second image.

ЛЕДНИКОВОЕ ГЕОМОРФОЛОГИЧЕСКОЕ КАРТОГРАФИРОВАНИЕ

Изобретение относится к области геокриологии и может быть использовано в процессе ледникового геоморфологического картографирования. Данные изобретения являются реализациями различных технологий для способа картографирования ледниковой геоморфологии. Может быть принято изображение исследуемого района, полученное с помощью спутника. Может быть принята цифровая модель возвышений исследуемого района. Равнины и гряды могут быть идентифицированы на цифровой модели возвышений. Болота и лес могут быть идентифицированы на изображении, полученном с помощью спутника. Гляциологическая карта может быть сформирована, имея ледниковые признаки, на основе идентифицированных равнин, гряд, болот и леса. Технический результат - повышение точности идентификации элементов ледникового рельефа. 3 н. и 11 з.п. ф-лы, 14 ил., 4 табл.

СЕЛЬСКОХОЗЯЙСТВЕННАЯ РАБОЧАЯ МАШИНА

Способ регулирования рабочих параметров почвообрабатывающего орудия (варианты) и считываемый компьютером носитель для долговременного хранения информации для осуществления способа регулирования рабочих параметров почвообрабатывающего орудия (варианты)

Группа изобретений относится к сельскому хозяйству. Способ регулирования рабочих параметров почвообрабатывающего орудия, имеющего по меньшей мере один узел обработки почвы, выполненный с возможностью обработки почвы в поле при проходе по ней почвообрабатывающего орудия, предусматривает выполнение первой калибровки почвообрабатывающего орудия на основе предварительно сформированной информационной базы, включающей в себя информацию по корреляции данных ранее проведенных наблюдений с физическими свойствами почвы и остатков на других полях; перемещение почвообрабатывающего орудия по полю таким образом, чтобы по меньшей мере один узел обработки почвы обрабатывал почву; получение изображения поля с помощью камеры, установленной на почвообрабатывающем орудии; сравнивание информации из указанного полученного изображения с указанной предварительно сформированной информационной базой; идентифицирование остатков на указанном изображении на основе результата сравнения информации из полученного изображения ...

Verfahren zur selbsttätigen Einstellung von Dreschparametern eines Mähdreschers während der Ernte unter Verwendunq einer Anordnung zur Erkennung von Strohqualität

Verfahren zur selbsttätigen Einstellung von Dreschparametern eines Mähdreschers während der Ernte, mit folgenden Schritten: Untersuchen eines vom Mähdrescher abgegebenen Strohschwads (20) während der Ernte durch ein elektrooptisches Gerät, Bestimmen eines Signals hinsichtlich der Strohqualität des Strohschwads (20) anhand eines Ausgangssignals des elektrooptischen Geräts und Übersenden des Signals hinsichtlich der Strohqualität des Strohschwads (20) an eine Kontrolleinheit des Mähdreschers, welche anhand der Daten die Einstellung der Dreschtrommel- oder Rotorgeschwindigkeit und/oder des Dreschspalts des Mähdreschers im Sinne einer Verbesserung der Strohqualität modifiziert, dadurch gekennzeichnet, dass das elektrooptische Gerät eine in einem Handgerät oder an einem eine Ballenpresse (12) ziehenden Traktor (10) oder an einer Ballenpresse (12) angebrachte Kamera (26, 26') ist, die ein Bild des Strohschwads (20) aufnimmt und mit einem Bildverarbeitungssystem (36) verbunden ist, das in dem ...

Improved method, apparatus and system for estimating the mass of a stockpile

Sensor data processing

VERFAHREN UND EINRICHTUNG ZUR ERMITTLUNG DER POSITION VON WACHSENDEN NUTZPFLANZEN

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Method and apparatus for monitoring plant health state

A method for monitoring a plant health state, relating to the technical field of intelligent agriculture, for intelligently judging a plant health state and timely prompting farmland management staff to perform prevention and treatment for plants in a poor state of plant health. The method for monitoring a plant health state comprises: carrying out first judgement according to plant health state information; when a judgement result is that there is a risk of poor plant health, carrying out second judgement according to second plant health state information in the orientation where the plants at risk of poor plant health are located, to obtain accurate plant health state information, so that farmland management staff can learn the current state of the plants in farmland timely and prevent and treat same timely. An apparatus applying the method for monitoring plant health state. The apparatus is used for monitoring plant health state.

EFFICIENT FUNGI DISEASE DETECTION AND GRADING FOR LEAFY VEGETABLES USING OPTIMIZED IMAGE PROCESSING TECHNIQUES

The image processing techniques can be used in the plant disease detection. In most of the cases disease symptoms are seen on the leaves, stem and fruit. The plant leaf for the detection of disease is considered which shows the disease symptoms. Disease fungi take their energy from the plants on which they live. They are responsible for a great deal of damage and are characterized by wilting, scabs, mouldy coatings, rusts, blotches, and rotted tissue. Figure 1 explains the conceptual diagram that pre-processing stage used conventional methods of filtration like median filter, wiener filter and Gaussian filter and we have proposed new methods of filtration like Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR) & Mutual Information (MI). The segmentation stage used conventional methods of filtration like Ostu, Watershed and Region growing and we have proposed new methods like Structural Similarity Index Motion (SSIM), Peak signal to Noise Ratio (PSNR), Accuracy and time. The feature ...

A method of determining and treating the health of a crop

System and method for detecting plant diseases

A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11)of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster is associated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined ...

Automatic driving system for grain processing, and automatic driving method and path planning method therefor

An automatic driving system for grain processing, and an automatic driving method and path planning method therefor. The automatic driving system for grain processing comprises a grain processing host (10), an image processing system (30), a path planning system (60), and an image acquisition apparatus (20). The image processing system (30) is arranged on the grain processing host (10) and acquires at least one image of farmland surrounding the grain processing host (10); the image processing system (30) recognizes, based on an image segmentation and recognition technique, a region in the image; the path planning system (60) plans, based on the region recognized by the image processing system (30), at least one driving planning path; and the grain processing host (10) automatically controls driving according to the driving planning path planned by the path planning system (60).

Shadow and cloud masking for remote sensing images in agriculture applications using multilayer perceptron

A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.

Arrangement and method for identifying and tracking logs

There is disclosed an arrangement for identifying and tracking a log. The arrangement comprises at least one imaging device that, when in operation, captures at least one image of the log, a Global Positioning System (GPS) receiver that, when in operation, determines a felling location of the log, a remote server arrangement that, when in operation, receives data related to captured at least one image and the determined felling location of the log, and processes the received data to create an identification information for the log, and a data processor that, when in operation, checks if a query matches with the identification information in the remote server arrangement, and retrieves identification information corresponding to the matched query.

Soil salinity at Yellow River Delta Inversion Method based on Landsat 8

Abstract The present invention provides a soil salinity at Yellow River Delta inversion method based on Landsat 8, with the following steps: S: collecting and acquiring soil salinity data; S2: getting and processing of remote sensing image data; S3: extracting and calculating reflectivity data of bands of Landsat 8 images; S4: conducting corresponding lg(R), 1/R transformation to each band and analyzing remote sensing reflectivity and remote sensing reflectivity after transformation and soil salt content to get a preliminary list of sensitive bands; S5: carrying out a multivariate linear regression analysis to reflectance of each band after lg(R) and 1g transformation and soil sat content acquired in step S1, and establishing a model; S6: applying an optimal prediction model established in the above-mentioned method to Landsat 8 remote sensing images of a research region Yellow River Delta to get a Landsat 8 soil salinity remote sensing inversion image. When inversing soil salinity with ...

Method for Predicting Winter-Spring Stock Abundance of Ommastrephes Bartramii in the Northwest Pacific Ocean Based on Grey System

The invention discloses a method for predicting winter-spring stock abundance of Ommastrephes bartramii in the Northwest Pacific Ocean Based on a grey system. The method comprises steps of: (1) obtaining four sea environment and climate factors of nino3.4 anomaly, PDO data, a sea surface temperature SST and a chlorophyll concentration Chl-a through a remote sensing satellite; (2) performing grey correlation analysis on four sea environment and climate factors; (3) selecting four factors having a highest correlation degree according to a grey correlation analysis result, wherein the four factors include: an average sea surface temperature of spawning ground in March, Pacific Decadal Oscillation in January, nino3.4 anomaly in April and an average chlorophyll concentration in April; (4) establishing 8 prediction models based on the grey system according to four selected factors; and (5) performing validity check on 8 prediction models to select a grey correlation model GM (1,4) structure as ...

DISEASE DETECTION USING IOT AND MACHINE LEARNING IN RICE CROPS

DISEASE DETECTION USING IOT AND MACHINE LEARNING IN RICE CROPS Agricultural development is the vital form of income and a major part of an Indian economic growth is dependent on crop cultivation. In order to improve crop production and benefit, accurate diagnosis of plant leaf diseases is crucial. For several causes one of the main concerns for the aforementioned issue is crop disorders, farm workers are still suffering losses in growing crops. This is related to inadequate of information on the infection and the appropriate pesticides or insecticides to combat the infection. But identifying the actual illness and supplying the right treatment necessitates professional assistance or advanced experience to manage the illness. Farming professionals detect most diseases by analyzing visible indications. Nevertheless, producers have little exposure to the specialists. Rice grains often affected by Rice Blast and Bacterial Blight hence contributes to decline in rice crops productivity. This ...

A Single Tree Crown Segmentation Algorithm Based on Super-pixels and Topological Features in Aerial Images

Abstract The invention discloses an individual tree crown(ITC) segmentation algorithm based on aerial images using super-pixel and topological features, which comprises the following steps: performing Simple Linear Iterative Clustering (SLIC) superpixel segmentation on original aerial images, and simultaneously acquiring the coronal boundaries of the images via holistically-nested edge detection (HED) network; Calculating three similarity measurement indexes between two adjacent superpixels, i.e., the difference between RGB average values of two adjacent superpixels, the number of intersecting pixels of two adjacent superpixels, and the number of intersecting boundary pixels obtained from HED network, by which the similarity weights between two adjacent superpixels are constructed; Conducting a superpixel neighborhood connected graph based on the center point of each superpixel, and the minimum spanning tree(MST) is extracted from the connected graph to generate the connected tree of aerial ...

BOOM MOUNTED AGRICULTURAL MACHINE VISION PERSPECTIVE CORRECTION

In an agricultural machine vision system for the selective treatment of a field employing a boom deployed sensor and treatment device, the system utilizes at least one image sensor mounted on the boom to produce base image data. A boom angle detector detects boom height which is used to correct the base image data into remapped image data including a region of interest to produce corrected image data producing a regularized image of the region of interests for treatment, independent of boom tilt.

METHOD AND SYSTEM FOR PLANT STRESS DETERMINATION AND IRRIGATION BASED THEREON

The invention provides various methods of plant stress determination including using a computer-based camera system (84) having thermal imaging (46) and visual imaging (50) to capture foliage at close proximity of at least one plant (70) to provide high resolution images/video (72, 74) thereof; analyzing both thermal and visual images/video (72, 74) therefrom to form a composite image; determining the thermal activity of the composite image/video and photosynthesis state of said at least one plant (70); and deriving the plant stress from said determination. A handheld computer-based camera system (84) is also disclosed having a smartphone (86) and thermal imaging device (100).

GENERATION OF SYNTHETIC HIGH-ELEVATION DIGITAL IMAGES FROM TEMPORAL SEQUENCES OF HIGH-ELEVATION DIGITAL IMAGES

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

METHOD AND SYSTEM FOR PICKING UP AND COLLECTING PLANT MATTER

The invention relates to a method and system for picking up and collecting plant matter, in particular plant embryos. To pick up the plant matter, a pick-up unit is used that is mounted to a robotic arm. According to the invention, two separate imaging steps are performed at two different positions of the pick-up unit. The first imaging step is performed to identify an isolated piece of plant matter. The second imaging step is performed when the pick-up unit is at a confirming position and enables a verification of whether a piece of plant matter has been picked up or not. The confirming position is in between the position of the pick-up unit for picking up plant matter and the position for depositing plant matter in suitable receptacles.

WEED CONTROL SYSTEMS AND METHODS, AND AGRICULTURAL SPRAYER INCORPORATING SAME

A weed control system (2) for an agricultural sprayer (1 ) comprising a camera (3) and a spraying unit (4) with several supply modules, a nozzle (9) and a controller module to receive a weed species detection signal and to control the spraying of chemical agent. The weed control system (2) also comprises a weed species identification unit (5) with a communication module, a memory module and a processing module having several parallel processing cores. Each parallel processing core performs a convolution operation between a sub-matrix constructed from nearby pixels of the image and a predefined kernel stored in the memory module to obtain a feature representation sub-matrix of the pixel values of the image. The processing module computes a probability of presence of a weed species from the feature representation matrix and generates a weed species detection signal.

SYSTEMS AND METHODS FOR DETERMINING STATISTICS OF PLANT POPULATIONS BASED ON OVERHEAD OPTICAL MEASUREMENTS

This disclosure describes a system and a method for determining statistics of plant populations based on overhead optical measurements. The system may include one or more hardware processors configured by machine-readable instructions to receive output signals provided by one or more remote sensing devices mounted to an overhead platform. The output signals may convey information related to one or more images of a land area where crops are grown. The one or more hardware processors may be configured by machine-readable instructions to distinguish vegetation from background clutter; segregate image regions corresponding to the vegetation from image regions corresponding to the background clutter; and determine a plant count per unit area.

A METHOD TO QUANTIFY FIRE RISK TO STRUCTURES

A system and method of quantifying fire risk posed by vegetation or other types of fire fuels to structures and a process for facilitating the measurement are disclosed. The method utilizes protection perimeter zones around the structures, summarizing vegetation information within the zones and analyzing fire risk to structures.

WORK LAYER IMAGING AND ANALYSIS FOR IMPLEMENT MONITORING, CONTROL AND OPERATOR FEEDBACK

A soil imaging system having a work layer sensor disposed on an agricultural implement to generate an electromagnetic field through a soil area of interest as the agricultural implement traverses a field. A monitor in communication with the work layer sensor is adapted to generate a work layer image of the soil layer of interest based on the generated electromagnetic field. The work layer sensor may also generate a reference image by generating an electromagnetic field through undisturbed soil. The monitor may compare at least one characteristic of the reference image with at least one characteristic of the work layer image to generate a characterized image of the work layer of interest. The monitor may display operator feedback and may effect operational control of the agricultural implement based on the characterized image.

VISUAL SEGMENTATION OF LAWN GRASS

This invention provides a method for identifying lawn grass comprising capturing an image of the terrain in front of a mower, segmenting the image into neighborhoods, calculating at least two image statistics for each of the neighborhoods, generating a binary representation of each image statistic. The binary representation of each image statistic is generated by comparing the calculated image statistic values to predetermined image statistic values for grass. The method further comprises weighting each of the binary representations of each image statistic, and summing corresponding neighborhoods for all image statistics. A binary threshold is applied to each of the summed neighborhoods to generate a binary map representing grass containing areas and non-grass containing areas.

DEVICE AND METHOD FOR DETECTING A PLANT AGAINST A BACKGROUND

A device for detecting a plant against a background includes a means (10) for providing a plurality of different photographs of the plant leaf against the background, the photographs differing in that image points of the different photographs which relate to the same location of the plant leaf are illuminated with different levels of brightness, a means (12) for selecting such image points, from the different photographs, whose levels of brightness are within a predetermined range, an image point of a first photograph being selected for a first location of the plant leaf, and an image point of a second, different photograph being selected for a different location of the plant leaf so as to obtain a representation of the plant leaf against the background, said representation being composed of and/or merged from different photographs, and a means (14) for segmenting the composite photograph so as to obtain a segment representation comprising the plant leaf without the background or the background ...

SYSTEMS AND METHODS CONTROL AGRICULTURAL

Foliar drug spraying device and method based on plant spatial prescription map

A three-dimensional plant projection area calculation method

SYSTEM AND METHOD FOR ESTIMATING AN AMOUNT OF HARVESTING VITICULTURAL OPERATING WITHIN A

FIRE DETECTION METHOD AND APPARATUS

The present invention relates to a fire detection method and apparatus for accurately detecting a fire. The fire detection method includes the steps of: obtaining RGB data from an image; calculating at least one index related to the attributes of fire using the obtained RGB data; and determining the occurrence of a fire using the index.

MULTI-VARIABLE MODEL FOR IDENTIFYING CROP RESPONSE ZONES IN A FIELD

L'invention concerne un appareil et un procédé implémenté par un ordinateur définissant les zones d'un champ dans lesquelles une culture ou une autre végétation est cultivée en fonction de leur capacité sélective à faire pousser une telle végétation pendant une saison de croissance, ou une période de temps présélectionnée plus courte. Le procédé consiste à exécuter un certain nombre de mesures à des intervalles de temps séparés sur une image aérienne d'un champ, à enregistrer les données de la géographie du champ, à normaliser les données, ainsi qu'à les convertir en un index végétatif indicatif de la présence de végétation dans le champ, à comparer les données afin d'identifier des petits groupes de valeurs similaires, et à classifier des petits groupes et des images pour apprendre comment les différentes zones de champ ont réagi au cours de la saison de croissance de végétation. Au moyen de ce procédé, le champ peut être divisé en un certain nombre de zones, dénommées zones de réaction ...

Satellite image classification across multiple resolutions and time using ordering constraint among instances

A method includes classifying low-resolution pixels of a low-resolution satellite image of a geographic area to form an initial classification map and selecting at least one physically-consistent classification map of the low-resolution pixels based on the initial classification map. A water level associated with at least one of the physically-consistent classification maps is then used to identify a set of high-resolution pixels representing a perimeter of water in the geographic area.

INFORMATION DISPLAY APPARATUS, METHOD FOR CONTROLLING INFORMATION DISPLAY APPARATUS, AND STORAGE MEDIUM

An apparatus comprises a unit that acquires observation data indicating an observation value representing a state of a crop, the observation value being associated with any of a plurality of observation types representing types of observation objects, the observation value being obtained by observing a crop; and a control unit that visualizes and displays the observation value at a position on a map where the observation has been performed, the observation value being indicated by the acquired observation data, wherein when displaying the observation values simultaneously or in a switchable manner, in a case where the control unit visualizes the observation value exceeding or less than a predetermined value specified for each of the plurality of observation types, it visualizes and displays the observation value on the map by using an identical display form for each of the plurality of observation types.

Shape-based segmentation using hierarchical image representations for automatic training data generation and search space specification for machine learning algorithms

A system and various methods for processing an image to produce a hierarchical image representation model, segment the image model using shape criteria to produce positive and negative training data sets as well as a search-space data set comprising shapes matched to a search query provided as input, and using the training data sets to train a machine learning model to improve recognition of shapes that are similar to an input query without being exact matches, to improve object recognition.

System and Method for Detecting Plant Diseases

A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11) of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster is associated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined ...

METHODS AND SYSTEMS FOR ANALYZING A FIELD

Methods and systems for analyzing a field. The methods and systems acquire a thermal image indicative of thermal energy emitted by the soil and/or plants in the field and process the thermal image to assess variations in certain characteristics of the soil and/or plants.

Ground control point extraction from planting data

The present subject matter provides a technical solution for various technical problems associated with precision agriculture imagery. One solution to improving precision agriculture imagery includes identification and use of ground control points. A ground control point may include the association of a visible feature in an image of an agricultural field with a geospatial coordinate. A ground control point may be used as a common reference point between two aerial images, thereby improving alignment between the two images during stitching. The ground control points may be generated using planting prescription data or as-planted data, which may include one or more intentionally unplanted regions within an agricultural filed. Multiple aerial images may be stitched together using these ground control points to generate a precisely georeferenced output image, thereby improving the accuracy of the precision agriculture output image.

BALE IDENTIFICATION USING PHOTOGRAPHIC IMAGE OF TWINE PATTERN

A method of identifying a bale of material includes capturing an initial image of a region of an initial bale with an initial image sensor and identifying a unique band pattern of at least one multi-colored band of the initial bale. The identified unique band pattern is associated with the initial bale. Data related to the initial bale is associated with the identified unique band pattern. The identified unique band pattern of the initial bale may then be used as an identifier of the initial bale. A second image of the bale is captured and analyzed to determine if the second image includes the unique band pattern of the at least one multi-colored band, thereby identifying the bale in the second image as the initial bale. The data associated with the initial bale may then be retrieved by an operator. 1. A method of identifying a bale of material bound together by at least one multi-colored band , the method comprising:capturing an initial image of a region of an initial bale with an initial image sensor;identifying a unique band pattern of the at least one multi-colored band of the initial bale from the initial image with a computing device;associating the identified unique band pattern with the initial bale with the computing device; andsaving the captured initial image of the initial bale and the identified unique band pattern associated with the initial bale in a memory of the computing device, such that the identified unique band pattern of the initial bale is used as an identifier of the initial bale.2. The method set forth in claim 1 , further comprising associating data related to the initial bale with the identified unique band pattern.3. The method set forth in claim 2 , further comprising capturing a second image of a second region of a second bale having a second indicia indicating the second region with a second image sensor.4. The method set forth in claim 3 , further comprising comparing the second image of the second bale to the unique band pattern ...

METHOD AND APPARATUS FOR GENERATION AND EMPLOYMENT OF PARCEL SUSTAINABILITY ATTRIBUTES FOR LAND PARCEL VALUATION

A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a sustainability metric.

System and method using image based machine learning process for earth observation and analysis

A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest ...

INFRARED AERIAL THERMOGRAPHY FOR USE IN MONITORING PLANT HEALTH AND GROWTH

A method and system for monitoring the health and growth of plants in a field. The method and system acquire a thermal image indicative of thermal energy emitted by the plants and process the thermal image to assess variations in the temperatures among the plants.

RAPID STALK STRENGTH ASSESSMENT

A system for post-harvest or at-harvest determination of pre-harvest strength of a corn stalk wherein the system comprises a stalk stump cutter structured and operable to cut a discarded post-harvest stalk stump to provide a substantially flat and even cross-sectional surface of the stalk stump, an imaging device structured and operable to acquire image data of the stalk stump cross-section, and a computer based data processing system structured and operable to analyze the image data and determine a pre-harvest stalk strength of the corresponding stalk.

DETERMINATION SYSTEM, METHOD AND PROGRAM

Provided is a decision system (1) which includes: a control unit (11) in a computer device (10) executes an image acquisition module (111) to issue an instruction to a capture unit (20) to enable the capture unit (20) to acquire images of a same object in a plurality of directions; next, the control unit (11) executes a decision module (112) to analyze the images acquired from the directions, and decide, with reference to a decision criterion database (134) stored in a storage unit (13), whether decision criteria are satisfied, where the decision criteria are separately determined for the directions in which the images are acquired; next, the control unit (11) executes a provision module (113) to provide, based on results of decisions, a decision result for the object.

COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR IMAGE TEXTURE DETERMINATION

This disclosure relates generally to image processing, and more particularly to image texture determination. In one embodiment a processor a memory coupled to the processor, wherein the processor coupled with a plurality of modules stored in the memory: At least one image having a plurality of pixels is accepted. Any noise is removed from the image to obtain at least one noise free image. The at least one noise free image is converted to at least one gray scale image. Horizontal and Vertical Gradient for plurality of pixels of the at least one gray scale image are computed. Gradient magnitude is calculated for the generated gradient. Histogram of the gradient magnitude is generated based on the gradient magnitude, and the plurality of generated histograms are compared with a plurality of predetermined histograms.

CROP YIELD PREDICTION PROGRAM AND CULTIVATION ENVIRONMENT ASSESSMENT PROGRAM

A crop yield prediction program includes: a degree-of-association acquisition step of acquiring in advance a degree of association between a combination of reference image information which is a captured image of a growing crop and reference soil information about a soil in which the crop is planted and a yield of the growing crop as harvested for the combination, the degree of association being represented in three or more levels; an information acquisition step of, when making a new prediction of the yield of the crop, capturing an image of a new growing crop to acquire image information and to acquire soil information about a soil in which the crop is planted; and a prediction step of predicting a yield of the new growing crop with reference to the degree of association acquired at the degree-of-association acquisition step and based on the image information and the soil information.

Agricultural sprayer with real-time, on-machine target sensor

An agricultural applicator, that applies material to an agricultural field, includes an on-board, real-time image sensor that senses targets for the material to be applied. A controller controls applicators, such as nozzles or other applicators, to apply the material to the sensed targets.

Smart lawn mower

A smart lawn mower comprises a traveling control module configured to control the traveling and steering of the mower, an image capturing module configured to capture the surrounding images of the mower, an operation module configured to provide a surrounding-determination information, and a storage module configured to store the surrounding-determination information. The operation module determines a grass area by analyzing the surrounding images captured by the image capturing module. The mower defines a grass area accurately without a predetermined boundary.

Method for estimating chlorophyll content of a leaf

A method of estimating chlorophyll content of a leaf including providing a device wherein the device includes a mobile computing device with a digital camera; and a peripheral removably attached to the mobile computing device, the peripheral including a light source aligned with the digital camera, wherein the peripheral provides for a space between the light source and the digital camera; capturing by the digital camera a first image of the light source turned on without the leaf interposed between the digital camera and the light source; retrieving exposure data of the first image; capturing by the digital camera a second image with the leaf interposed between the digital camera and the turned on light source; retrieving exposure data of the second image; and using a processor, estimating the chlorophyll content of the leaf based on the retrieved exposure data of the first and second images.

Method and system for harvesting, packaging, and tracking crop material

A method of harvesting crop material includes capturing an image of a plurality of regions 28 of a field 50. The respective image of the regions 28 is analyzed to determine data related to constituent species of the crop material located within each of the regions 28. The data is associated with a region identifier 48 assigned to the respective one of the regions 28, and saved in a memory 42 of a computing device 30. The crop material in the field 50 is then harvested and formed into a bale. The harvested crop material formed into the bale is gathered from a subset 46 of the regions 28. A region identifier 48 of each of the regions 28 included in the subset 46 of the regions 28 is associated with the bale identifier 38, such that the data related to constituent species of the crop material included in the bale is associated with the bale and may be obtained by querying the computing device 30.

VEGETATION GROWTH CONDITION ANALYSIS METHOD, RECORDING MEDIUM ON WHICH PROGRAM IS RECORDED, AND VEGETATION GROWTH CONDITION ANALYZER

It is aimed to enable analyzing vegetation growth conditions at multiple times of a year accurately using radar images obtainable from a flying body such as artificial satellites, etc. A plurality of radar images of a ground surface of a same target area, which have been taken at multiple times of a year by a radar device mounted on a flying body, are acquired. The acquired plurality of radar images is stored in a map database. While using as a criterion image a radar image of the plurality of radar images stored in the map database, taken at a predetermined time in the multiple times of a year, other radar images than the criterion image of the plurality of radar images, taken at other times than the predetermined time in the multiple times of a year, are aligned with the criterion image, respectively. Then, backscatter coefficients of specified areas in the plurality of radar images are extracted. Based on a backscatter coefficient of a specified area in the criterion image of the plurality ...

Method and device for performing inversion of crop leaf area index

The method and device for performing inversion of a crop leaf area index are provided. The method includes acquiring remote sensing image data, performing pre-processing on the remote sensing image data to obtain hyperspectral data and inputting the hyperspectral data into a pre-generated target reference inversion model to obtain a leaf area index outputted by the target reference inversion model. The target reference inversion model is obtained by optimizing a reference inversion model based on target training samples. The target training samples are obtained by performing data augmentation on original training samples using a Dual-Generative Adversarial network (DualGAN).

Method for synchronously diagnosing nitrogen, potassium, and magnesium deficiency based on chlorophyll distribution characteristics on leaf surface

A method for synchronously diagnosing nitrogen, potassium and magnesium element deficiency on the basis of distribution characteristics of leaf chlorophyll on the leaf surface: first, segmenting a leaf surface area of a leaf to be measured into a plurality of small areas; then, extracting regional distribution characteristics of chlorophyll, and extracting chlorophyll content mean values, chlorophyll content variances, chlorophyll content maximum values, and chlorophyll content minimum values corresponding to all pixel points in the small areas in a leaf chlorophyll leaf surface distribution diagram by using a hyperspectral image technology; constructing a nitrogen-potassium-magnesium deficiency diagnosis model on the basis; and diagnosing nitrogen, potassium and magnesium deficiency of said leaf according to the model. According to the method, the limitation that an element deficiency diagnosis method based on chlorophyll content cannot synchronously diagnose the deficiency of nitrogen ...

Method and system for collecting image data

Computationally Efficient Method for Image Segmentation with Intensity and Texture Discrimination

A computationally efficient image segmentation method is provided that processes a grayscale digital image to more clearly show textures in the underlying object shown in the digital image. A grayscale digital image is converted to an intensity matrix based on the brightness of the pixels in the image, where each matrix element represents a pixel in the digital image and has a value corresponding to the intensity, i.e., the brightness, of that pixel. The value of each matrix element is compared to the value of its nearest neighbor matrix element, and the pixel represented by the matrix element is categorized as being “dark” or “bright” based on its value, and is categorized as being “smooth” or “rough” based on the values of the nearest neighbor matrix elements. As each pixel is categorized, it is assigned a shading level corresponding to the brightness/texture matrix element value. A processed image having only the assigned shading levels is then produced, with the processed image indicating textures of the underlying object shown in the original grayscale digital image.

Binocular-vision-based method for tracking fruit space attitude and fruit space motion

A binocular-vision-based method for tracking fruit space attitude and fruit space motion, the method comprising: establishing a connected base coordinate system by taking a junction of a fruit and a fruit stem as an origin; statically photographing a feature point on the surface of the fruit and a point of the connected base coordinate system established at the junction of the fruit and the fruit stem; storing a photographed image; acquiring an inherent relationship between the feature point and the connected base coordinate system; photographing dynamic motion of the fruit; acquiring absolute coordinates of the feature point on the surface of the fruit; calculating, according to the inherent relationship between the feature point and the connected base coordinate system, absolute coordinates of a point of the connected base coordinate system at each moment corresponding to each frame of image; and respectively calculating the displacement, instantaneous speed and instantaneous acceleration of the fruit, calculating swing angular displacement and swing angular acceleration of the fruit, and calculating a fruit torsion angular speed and a fruit torsion angular acceleration at the moment t. The study of a fruit motion state in the field of forest fruit harvest through vibration is performed, so that the motion of fruits can be better tracked.

Method and device for the characterization of living specimens from a distance

A method and a device for the characterization of living specimens from a distance are disclosed. The method comprises: acquiring an image of a living specimen and segmenting the image, providing a segmented image; measuring a distance to several parts of said image, providing several distance measurements, and selecting a subset of those contained in the segmented image. The method also processes the segmented image and the distance measurements referred to different positions contained within the segmented image by characterizing the shape and the depth of the living specimen and by comparing a shape analysis map and a depth profile analysis map. If a result of the comparison is comprised inside a given range, parameters of the living specimen are further determined including posture parameters, location or correction of anatomical reference points and/or body size parameters, and/or a body map of the living specimen is represented.

METHOD FOR MEASURING PLANT PLANTING DATA, DEVICE AND SYSTEM

A method for measuring plant planting data, and a method for planning operating route, device and system are provided, which relate to the technical field of plant protection. The method includes: training a deep network model using historical information of a planting region (S); receiving image information of the planting region (S); processing the image information of the planting region using a deep network model to obtain plant planting data (S); and outputting the plant planting data (S). The method for planning operating route utilizes the plant planting data obtained by the measuring method to plan an operating route. 1. A method for measuring plant planting data , comprising:receiving image information of a planting region; andprocessing the image information of the planting region using a preset recognition model to obtain plant planting data.2. The method for measuring plant planting data as claimed in claim 1 , wherein the image information comprises one or more piece of: mapping image information claim 1 , map information claim 1 , and picture information.3. The method for measuring plant planting data as claimed in claim 1 , wherein the preset recognition model is a deep network model claim 1 , and before receiving image information of a planting region claim 1 , the method for measuring plant planting data further comprises:receiving historical information of the planting region, the historical information of the planting region comprising historical image information and historical plant calibration information corresponding to the historical image information;extracting historical plant graphic features from the historical image information;processing the historical plant graphic features and the historical plant calibration information using a deep learning mode to obtain a loss value of the deep network model;optimizing the loss value of the deep network model using the historical plant calibration information to obtain a deep network model, the ...

Automatic Crop Health Change Detection and Alerting System

A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

ROOT IMAGING DEVICE

A root imaging system is disclosed which includes a robotic arm, a camera coupled to the robotic arm, the robotic arm configured to insert the camera into the soil at predefined coordinates, a processor system configured to receive user-defined data about a plant of interest, define insertion points about the plant of interest based on the user-defined data, control the robotic arm to thereby insert the camera into the soil at defined insertion points, obtain images from the camera positioned at the insertion points, analyze the images for presence of roots, and identify the images having roots and combine that with image 3D location information for 3D structure reconstruction of the entire root structure. 1. A root imaging system , comprising:a robotic arm;a camera coupled to the robotic arm, the robotic arm configured to insert the camera into the soil at predefined coordinates; receive user-defined data about a plant of interest,', 'define insertion points about the plant of interest based on the user-defined data,', 'control the robotic arm to thereby insert the camera into the soil at defined insertion points,', 'obtain images from the camera positioned at the insertion points,', 'analyze the images for presence of roots, and', 'identify the images having roots and combine that with image 3D location information for 3D structure reconstruction of the entire root structure., 'a processor system configured to'}2. The root imaging system of claim 1 , the user-defined data includes stem diameter claim 1 , a maximum diameter claim 1 , and a depth of the root.3. The root imaging system of claim 2 , the insertion points are defined based on a minimum resolution between two adjacent insertion points based on diameter of the camera.4. The root imaging system of claim 3 , the minimum resolution between two adjacent insertion points is 2 times the camera diameter.5. The root imaging system of claim 4 , the insertion points are defined based on a cylindrical coordinate ...

Method and apparatus for using a regionally specific spectral model to identify the spectral bands for use for isolating remotely sensed materials of interest

A system and method for collecting spectral data of a region of interest with a sensor is described. In one embodiment, the method comprises generating a simulated spectral representation of a region of interest, identifying at least one of the plurality of materials as a material of interest within the region of interest, identifying other of the plurality of materials not identified as a material of interest as background materials within the region of interest, selecting a subset spectral portion of the spectral data according to the simulated spectral representation of the material of interest and the simulated spectral representation of the background materials within the region of interest, and configuring the sensor to collect a subset spectral portion of the spectral data.

Multispectral Filters

A method and system including: an aerial vehicle including: a first camera comprising a first sensor having at least red, green, and blue color channels, where the blue color channel is sensitive to near-infrared (NIR) wavelengths; a first optical filter disposed in front of the first sensor, wherein the first optical filter is configured to block wavelengths below green, between red and NIR, and longer wavelength NIR; a processor having addressable memory in communication with the first camera, where the processor is configured to: capture at least one image of vegetation from the first camera; provide red, green, and NIR color channels from the captured image from the first camera; and determine at least one vegetative index based on the provided red, green, and NIR color channels. 1. A system comprising: a first camera comprising a first sensor;', 'a second camera comprising a second sensor;', 'an incident light sensor disposed distal from the first camera and the second camera, wherein the incident light sensor is configured to capture incident light data; and', capture at least one image of vegetation from the first camera;', 'capture at least one image of vegetation from the second camera;', 'provide red, green, and near-infrared (NIR) color channels from the captured image from the first camera;', 'determine at least one vegetative index based on the provided red, green, and NIR color channels; and', 'calibrate the captured image of vegetation from the first camera and the captured image of vegetation from the second camera based on the determined at least one vegetative index and the captured incident light data., 'a processor having addressable memory in communication with the first camera, the second camera, and the incident light sensor, wherein the processor is configured to], 'an aerial vehicle comprising2. The system of claim 1 , wherein the first sensor of the first camera comprises at least red claim 1 , green claim 1 , and blue color channels claim ...

METHOD FOR MONITORING GROWTH OF PLANTS AND GENERATING A PLANT GROW SCHEDULE

One variation of a method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility. 1. A method for monitoring growth of plants within a facility comprising:accessing a series of ambient conditions captured by a fixed sensor unit, arranged proximal a grow area within the facility, at a first frequency during a first time period;writing the series of ambient conditions to a corpus of plant records associated with plants occupying a group of modules within the grow area based on known locations of modules within the grow area, the corpus of plant records comprising a first set of plant records associated with a first set of plants occupying a first module, in the group of modules, during the first time period;dispatching a mover to autonomously deliver the first module from the grow area to a transfer station; interpreting a final outcome of the plant based on plant-level data captured by the transfer station while transferring the plant out of the first module during a second time period succeeding the first time period; and', 'writing the final outcome of the plant to a plant record, in the first set of plant records, ...

SYSTEMS AND METHODS TO DETERMINE OBJECT POSITION USING IMAGES CAPTURED FROM MOBILE IMAGE COLLECTION VEHICLE

An object identification method is disclosed. The method includes obtaining images of a target geographical area and telemetry information of an image-collection vehicle at a time of capture, analyzing each image to identify objects, and determining a position of the objects. The method further includes determining an image capture height, determining a position of the image using the capture height and the telemetry information, performing a transform on the image based on the capture height and the telemetry information, identifying the objects in the transformed image, determining first pixel locations of the objects within the transformed image, performing a reverse transform on the first pixel locations to determine second pixel locations in the image, and determining positions of the objects within the area based on the second pixel locations within the captured image and the determined image position. 1. A method , comprising:obtaining a first image of a geographical area;performing a homography transform on the first image to generate a second image having uniform-pixel-distances based on a capture height and avionic telemetry information associated with an aerial vehicle that captured the first image;performing image recognition on the second image to identify an object in the second image based on trained object parameters;determining a first pixel location of the object within the second image;performing a reverse homography transform on the first pixel location to determine a second pixel location in the first image for the object;determining a position of the object within the geographical area based on the second pixel location within the first image and an image position associated with the aerial vehicle capturing the image; andstoring the determined position of the object.2. The method of claim 1 , wherein obtaining the first image of the geographical comprises:capturing, via a camera on the aerial vehicle, the first image; andrecording the avionic ...

MOISTURE AND VEGETATIVE HEALTH MAPPING

A vegetative health mapping system which creates two- or three-dimensional maps and associates moisture content, soil density, ambient light, surface temperature, and/or additional indications of vegetative health with the map. Moisture content is inferred using radar return signals of near-field and/or far-field radar. By tuning various parameters of the one or more radar (e.g. frequency, focus, power), additional data may be associated with the map from subterranean features (such as rocks, soil density, sprinklers, etc.). Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments. 1. A device comprising: receiving a plurality of coordinates that define a bounded region of interest;', 'determining, based at least in part on the plurality of coordinates, a trajectory to sweep the bounded region of interest using at least one of minimal energy or minimal motion;', 'receiving sensor data from a sensor associated with the device;', 'determining, based at least in part on a portion of the sensor data, information about vegetation proximate the device;', 'determining, based at least in part on the information about vegetation, a location of the device; and', 'controlling, based at least in part on the location, the device to move along the trajectory., 'one or more non-transitory computer readable media storing instructions, which, when executed by one or more processors, cause the one or more processors to perform operations comprising2. The device of claim 1 , wherein the device further comprises a coupling configured to one or more of:couple the device ...

THREE-DIMENSIONAL MAP DISPLAY SYSTEM

A map database stores three-dimensional polygons of features, as well as water system polygons such as sea and lake and ground surface polygons. The map database stores map data in multiple levels having different levels of details, such as levels LVa to LVc. A procedure of displaying a three-dimensional map offsets the water systems relative to the ground surfaces, draws a map of a distant view area distant away from the viewpoint using map data at a low level of details, subsequently clears a depth buffer and newly draws a map of a close view area close to the viewpoint using map data at a high level of details. The offset is set to increase in the distant view area and decrease in the close view area. Increasing the offset in the distant view area avoids the occurrence of Z-fighting in the distant area from the viewpoint. 1. A three-dimensional map display system that displays a three-dimensional map , comprising:a map database that stores three-dimensional polygon data representing geography and a three-dimensional shape of each feature;an offset setting section that performs an offset process in an overlapping area of a first polygon and a second polygon representing substantially horizontal planes to shift the first polygon and the second polygon relative to each other in a height direction, so as to make a height difference between the first polygon and the second polygon; anda drawing controller that uses the three-dimensional polygon data and polygons processed by the offset setting section to draw the three-dimensional map by perspective projection viewed from a specified viewpoint position and in a specified gaze direction, whereinthe offset setting section shifts the first polygon and the second polygon to increase the height difference at a distant point from the viewpoint in the perspective projection than a close point to the viewpoint.2. The three-dimensional map display system according to claim 1 , whereinthe map database stores the three- ...

THREE-DIMENSIONAL MAP DISPLAY SYSTEM

A three-dimensional map is displayed in a bird's eye view with a stereoscopic effect of feature polygons by providing shading in an appropriate direction according to the gaze direction in a simulative manner. Shading wall polygons are set in addition to feature polygons in three-dimensional map data. The shading wall polygon is a virtual plate-like polygon provided vertically, for example, along a boundary of a feature polygon. When provided around the water system, the shading wall polygon is specified to be opaque on one surface viewed from the water system side and to be transparent on the opposite surface. The shading wall polygons are drawn along with the feature polygons in the process of displaying a map. The shading wall polygon is drawn in black, gray or the like only at a location where the surface specified to be opaque faces a gaze direction. 1. A three-dimensional map display system that displays a three-dimensional map , comprising:a drawing map database that is used to draw the three-dimensional map; anda display controller that refers to the drawing map database and displays the three-dimensional map as a bird's eye view from a viewpoint position looking down from a height and in a gaze direction, whereinthe drawing map database stores: feature polygon data used to draw feature polygons representing shapes of features to be drawn in the three-dimensional map; and shading wall polygon data used to display a shading wall polygon, which is a virtual plate-like polygon to express shading in the three-dimensional map, is set perpendicular to or inclined to a feature polygon for which the plate-like polygon is to be set, and is specified to be visible only from one surface of front and rear surfaces and to be transparent from the other surface, andthe display controller displays only the surface specified to be visible with respect to the shading wall polygon.2. The three-dimensional map display system according to claim 1 , whereinthe shading wall ...

INSPECTING PLANTS FOR CONTAMINATION

A method of inspecting plants for contamination includes generating a first series of images of a plant, identifying a region of interest displayed in the first series of images, comparing a color parameter of the region of interest to a color criterion associated with a type of contamination, comparing a morphological parameter of the region of interest to a reference parameter associated with the type of contamination, and upon determining that the color parameter meets the color criterion and that the morphological parameter sufficiently matches the reference parameter, identifying the region of interest as a region of contamination on the plant. The method further includes transmitting an instruction to lift a cutter of a harvester up from a planting bed to avoid harvesting the plant in response to identifying the region of interest as the region of contamination, and generating a second series of images of an additional plant. 1. A method of inspecting plants for contamination , the method comprising:generating a first series of images of a plant disposed along a planting bed using a camera mounted to a frame being moved along the planting bed by a harvester;identifying a region of interest displayed in the first series of images from feature boundary data defined by color regions associated with the first series of images;comparing a color parameter of the region of interest to a color criterion associated with a type of contamination;comparing a morphological parameter of the region of interest to a reference parameter associated with the type of contamination;upon determining that the color parameter meets the color criterion and that the morphological parameter sufficiently matches the reference parameter, identifying the region of interest as a region of contamination on a surface of the plant;in response to identifying the region of interest as the region of contamination, transmitting data comprising an instruction to lift a cutter of the harvester up ...

DETERMINING IMAGE FEATURE HEIGHT DISPARITY

A device to determine a height disparity between features of an image includes a memory including instructions and processing circuitry. The processing circuitry is configured by the instructions to obtain an image including a first repetitive feature and a second repetitive feature. The processing circuitry is further configured by the instructions to determine a distribution of pixels in a first area of the image, where the first area includes an occurrence of the repetitive features, and to determine a distribution of pixels in a second area of the image, where the second area includes another occurrence of the repetitive features. The processing circuitry is further configured by the instructions to evaluate the distribution of pixels in the first area and the distribution of pixels in the second area to determine a height difference between the first repetitive feature and the second repetitive feature. 1. A device to determine a height disparity between features of an image , the device comprising:a camera system to obtain an image, the image comprising a first repetitive feature and a second repetitive feature; and a first distribution element configured to determine a distribution of pixels in a first area of the image, the first area comprising an occurrence of the first repetitive feature and an occurrence of the second repetitive feature;', 'a second distribution element that is configured to determine a distribution of pixels in a second area of the image, the second area comprising another occurrence of the first repetitive feature and another occurrence of the second repetitive feature, and', 'a comparator element configured to evaluate the distribution of pixels in the first area and the distribution of pixels in the second area to determine a height difference between the first repetitive feature and the second repetitive feature., 'a machine controller comprising2. The device of claim 1 , wherein the camera system comprises a mono camera system.3. ...

Method and system for crop type identification using satellite observation and weather data

A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

INFORMATION GENERATION METHOD, INFORMATION GENERATION APPARATUS, AND PROGRAM

A specific environmental stress on vegetation is calculated highly accurately and relatively easily. For generation of environmental stress information, vegetation information is obtained first using an imaging signal of vegetation. Furthermore, reference vegetation information associated with vegetation information in a state of being free of, for example, a specific environmental stress is obtained. Moreover, as information associated with an environmental stress on the vegetation, difference information between vegetation information acquired from an imaging signal of vegetation in a state of being likely to have the specific environmental stress and the reference vegetation information is obtained. 1. An information generation method comprising:a vegetation information acquisition procedure of acquiring vegetation information using an imaging signal of vegetation; anda difference acquisition procedure of acquiring difference information between the vegetation information and reference vegetation information related to a specific environmental stress.2. The information generation method according to claim 1 , whereinthe vegetation information acquisition procedure includes obtaining vegetation information associated with a reference district set into a state of being free of the specific environmental stress from an imaging signal of the reference district, and obtaining vegetation information associated with a district to be measured set into a state of being likely to have the specific environmental stress from an imaging signal of the district to be measured, andas a reference vegetation information acquisition procedure, acquiring the reference vegetation information using the vegetation information associated with the reference district, andthe difference acquisition procedure includes performing computation of a difference between the vegetation information associated with the district to be measured and the reference vegetation information.3. The ...

CROP RESIDUE BASED FIELD OPERATION ADJUSTMENT

A crop residue monitoring system may include a harvester, a camera to capture an image of crop residue generated by the harvester, an analytical unit to derive a value for a crop residue parameter of the crop residue based upon an optical analysis of the image and a control unit to adjust a subsequent field operation based upon the value of the crop residue parameter. 1. A crop residue monitoring system comprising:a harvester;a camera to capture an image of crop residue generated by the harvester;an analytical unit to derive a value for a crop residue parameter of the crop residue based upon an optical analysis of the image; anda controller to adjust a subsequent field operation based upon the value of the crop residue parameter.2. The system of claim 1 , wherein the camera is carried by the harvester.3. The system of claim 1 , wherein the adjustment of the subsequent field operation by the controller comprises adjusting an operational parameter of the harvester.4. The system of further comprising a non-transitory machine-readable medium storing a crop residue parameter field map comprising the crop residue parameter claim 1 , wherein the adjustment of the subsequent field operation comprises adjusting an operational setting of an agricultural machine different than the harvester based upon the crop residue parameter field map.5. The system of further comprising a non-transitory machine-readable medium storing a crop residue parameter field map comprising the crop residue parameter claim 1 , wherein the adjustment of the subsequent field operation comprises adjusting a parameter of the subsequently applied material based upon the crop residue parameter field map.6. The system of claim 1 , or the harvester is to discharge the crop residue across a row having a width and wherein the crop residue parameter is derived for a first portion of the width of the row and wherein the analytical unit is to further derive a second value for the crop residue parameter for a ...

AGRICULTURAL WORK MACHINE

An agricultural work machine has a crop collection arrangement for separating and collecting crops from field vegetation, which crop collection arrangement has at least one crop cutting device, a crop conveying device arranged downstream thereof and a crop intake device arranged downstream thereof, and having a control device which has at least one sensor unit for optically detecting a crop flow, an image processing unit for processing images which are generated by the sensor unit based on the optically detected crop flow, and a data output unit for displaying the images processed by the image processing unit. The image processing unit generates a velocity characteristic map and a directional change characteristic map based on the images generated by the sensor unit. The two characteristic maps are utilized jointly or each individually by the control device to control processes in the agricultural work machine and/or in the crop collection arrangement. 1. An agricultural work machine , comprising:a crop collection arrangement for separating and collecting crops from field vegetation, the crop collection arrangement comprising at least one crop cutting device, a crop conveying device arranged downstream of the crop cutting device, and a crop intake device arranged downstream of the crop conveying device, anda control device comprising at least one sensor unit for optically detecting a crop flow, an image processing unit for processing images generated by the sensor unit based on the optically detected crop flow, and a data output unit for displaying the images processed by the image processing unit,wherein the image processing unit is configured to generate a velocity characteristic map and a directional change characteristic map based on the images generated by the sensor unit, andwherein the control device is configured to utilize the two characteristic maps jointly or each individually to control processes in the agricultural work machine and/or in the crop ...

APPARATUS FOR WEED CONTROL

An apparatus for weed control includes a processing unit with a least one sensor data of an environment. The processing unit analyzes at least some of the at least one sensor data to determine at least one ground property for each of a plurality of locations of the environment. The processing unit determines a power setting for activation of at least one electrode based weed control unit for each of the plurality of locations. The determination of the power setting for activation of the electrode based weed control unit comprises utilization of the at least one ground property associated with that location. An output unit outputs information useable to activate the at least one electrode based weed control unit. 2. The apparatus of claim 1 , wherein the at least one sensor data comprises at least one image claim 1 , and wherein the processing unit is configured to analyze the at least one image to determine at least one activation location of the plurality of locations for activation of the at least one electrode based weed control unit.3. The apparatus of claim 2 , wherein analysis of the at least one image to determine the at least one activation location comprises a determination of at least one location of vegetation.4. The apparatus of claim 3 , wherein determination of the power setting for activation of the at least one electrode based weed control unit for the at least one activation location comprises utilization of the determined at least one location of vegetation.5. The apparatus of claim 2 , wherein analysis of the at least one image to determine the at least one activation location comprises a determination of at least one type of weed.6. The apparatus of claim 5 , wherein determination of the power setting for activation of the at least one electrode based weed control unit for the at least one activation location comprises utilization of the determined at least one type of weed.7. The apparatus of claim 2 , wherein analysis of the at least one image ...

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, PROGRAM, AND GROUND MARKER SYSTEM

To make it possible to set a parameter, which is used for detection of a mark attached to a ground marker, according to the feature of the mark. 1. An information processing method executed by a computer , the method comprising:acquiring a captured image and information reading an altitude at which the captured image is captured;detecting a feature of a target object in the captured image;determining a size of the target object on a basis of a combination of the detected feature and the altitude information; anddetecting, with circuitry, whether the target object is a predetermined object based on the altitude information and the determined size of the target object.2. The information processing method according to claim 1 ,wherein the detecting includes distinguishing the target object from a plurality of candidate objects based on the determined size and the detected feature of the target object.3. The information processing method according to claim 1 ,wherein the feature is a color or a color arrangement applied to the target object.4. The information processing method according to claim 3 ,wherein, under a condition where different colors are applied to the target object so as to be adjacent to each other, hues of the different colors differ by a predetermined threshold or more.5. The information processing method according to claim 1 ,wherein the feature is a shape of the target object.6. The information processing method according to claim 5 ,wherein at least one aspect of the shape includes a plurality of circles having different radii arranged concentrically.7. The information processing method according claim 1 ,wherein the target object is a mark attached to a ground marker.8. The information processing method according to claim 1 ,wherein the acquiring includes acquiring the captured image from a UAV mounted camera.9. The information processing method according to claim 8 ,wherein the acquiring includes reception of a wireless communication from the UAV ...

INFERRING MOISTURE FROM COLOR

Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod. 1. A method implemented by one or more processors , the method comprising:obtaining a first digital image of at least one plant;segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; determining a color of the seedpod;', 'determining a number of seeds in the seedpod;', 'inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and', 'estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and, 'for each of the at least one seedpod in the first digital imagepredicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.2. The method according to claim 1 , further comprising determining a size of each of the at least one seedpod in the first digital image claim 1 ,wherein, for each of the at least one seedpod in the first digital image, the estimating the weight of the seedpod is further based on the size of the seedpod.3. The method according to claim 1 , further comprising determining a number of seedpods on each of the ...

SYSTEMS AND METHODS FOR DETECTING BLIGHT AND CODE VIOLATIONS IN IMAGES

A system for blight and code violation detection is provided. The system includes a machine learning model that is trained using photographs of properties that are associated with blight and code violations and photographs that are not associated with blight and code violations. A fleet of vehicles, such as trash trucks, are equipped with cameras to take photographs of properties along their routes. The trained model may generate score for each photograph that indicates whether or not a property is blighted or has code violations. Those properties with a score that exceeds a threshold may be provided to a reviewer who may verify the finding. If the finding is verified, the reviewer may issue a letter or citation to the owner of the property, and positive feedback may be provided to the model. If the finding is not verified, negative feedback may be provided to the model. 1. A method for detecting blighted properties or determining code violations comprising:receiving training data by a computing device, wherein the training data comprises a first plurality of images of blighted properties and a second plurality of images of non-blighted properties;generating, from the first and second plurality of images by the computing device, a model;receiving an image of a property by the computing device;using the model, generating a blight score for the property based on the image;based on the blight score, determining whether the property associated with the received image is a blighted property or a non-blighted property; andproviding the received image and blighted or non-blighted determination to a reviewer by the computing device.2. The method of claim 1 , wherein the property is one or more of a house claim 1 , a sidewalk claim 1 , a road segment claim 1 , or a tree.3. The method of claim 1 , further comprising:receiving feedback from the reviewer regarding the determination blighted or non-blighted determination; andadjusting the model based on the received feedback.4. ...

METHOD AND APPARATUS FOR MEASURING LEAF NITROGEN CONTENT

The present invention discloses a method and an apparatus for measuring leaf nitrogen content (LNC), and belongs to the spectral analysis and artificial intelligence (AI) field. The method includes the following steps: (1) obtaining a single-band image of a target leaf illuminated by a light source in a single feature band; (2) repeating step (1), to collect image information in four feature bands; (3) combining the collected images in the four feature bands into a four-channel spectral image; (4) training a deep learning model by using the spectral image and a corresponding nitrogen content label, to obtain a nitrogen content prediction model; (5) transplanting the trained nitrogen content prediction model into an AI control system; (6) collecting information about a to-be-predicted leaf sample, predicting nitrogen content by using an AI sensor equipped with the AI control system, and outputting the predicted nitrogen content. 1. A method for measuring leaf nitrogen content (LNC) , comprising the following steps:(1) obtaining a single-band image of a target leaf illuminated by a light source in a single feature band;(2) repeating step (1), to collect image information in four feature bands;(3) combining the image information in the four feature bands into a four-channel spectral image;(4) training a deep learning model by using a spectral image and a corresponding nitrogen content label, to obtain a nitrogen content prediction model;(5) transplanting the nitrogen content prediction model into an artificial intelligence (AI) control system; and(6) collecting information about a to-be-predicted leaf sample, predicting nitrogen content by using an AI sensor equipped with the AI control system to provide a nitrogen content prediction result, and outputting the nitrogen content.2. The method for measuring LNC according to claim 1 , further comprising the following steps:(7) outputting the nitrogen content prediction result through a serial port; and(8) displaying a ...

METHODS OF YIELD ASSESSMENT WITH CROP PHOTOMETRY

A method of evaluating one or more kernels of an ear of maize using digital imagery that includes acquiring a digital image of the one or more kernels of the ear of maize without the use of spatial reference points, processing the digital image to estimate at least one physical property of the one or more kernels of the ear of maize from the digital image, and evaluating the at least one kernel of maize using the estimate of the at least one physical property of the at least one kernel of maize. The method includes using one or more such digital images to estimate yield on a plant, management zone, field, county and country level. 1. A method of predicting yield of a corn crop , comprising:Obtaining one or more images of an ear of corn without the use of spatial reference points,Determining the number of kernels per ear, andCalculating total yield based on the image.2. The method of claim 1 , wherein an image of the corn ear is positioned within a target window on the viewing screen of an imaging device.3. The method of claim 2 , wherein the one or more images are still images from a video feed of the corn ear.4. The method of claim 2 , wherein the images are selected based on having a corn ear to target window surface area ratio between 70% to 90%.5. The method of claim 4 , wherein the corn kernels on the corn ear are segmented and counted.6. The method of claim 1 , wherein the number of kernels/ear is the median number of kernels from a set of still images.7. The method of claim 6 , wherein the image is a two dimensional image and the total number of kernels/ear is obtained by multiplying the number of kernels visible in the image by a calibration factor within the range of 2.25 to 2.50.8. The method of claim 1 , wherein total yield is calculated based on an estimate of the ears/acre claim 1 , which ears/acre estimate is based on the predicted test weight claim 1 , yield or number of ears per plant of the variety based on one or more of variety differences claim 1 ...

Object identification and sensing system and method

An object identification method is disclosed. The method includes obtaining images of a target geographical area and telemetry information of an image-collection vehicle at a time of capture, analyzing each image to identify objects, and determining a position of the objects. The method further includes determining an image capture height, determining a position of the image using the capture height and the telemetry information, performing a transform on the image based on the capture height and the telemetry information, identifying the objects in the transformed image, determining first pixel locations of the objects within the transformed image, performing a reverse transform on the first pixel locations to determine second pixel locations in the image, and determining positions of the objects within the area based on the second pixel locations within the captured image and the determined image position.

SYSTEMS, DEVICES, AND METHODS FOR IN-FIELD DIAGNOSIS OF GROWTH STAGE AND CROP YIELD ESTIMATION IN A PLANT AREA

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis. 1. A crop yield estimation system for detecting one or more properties of a plant area , the system comprising: capture at least 3D image data and 2D image data; and', 'generate geolocational data;, 'an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to receive the 3D image data, 2D image data, and geolocational data from the inspection system;', 'correlate the 3D image data, 2D image data, and geolocational data; and', 'analyze the data for the plant area; and, 'a datacenter configured toa dashboard configured to display a map with icons corresponding to the proper geolocational data and image data with the analysis.2. The crop yield estimation system of claim 1 , wherein the inspection system comprises at least one of an environment sensor unit claim 1 , a laser scanner unit claim 1 , an inertial movement unit claim 1 , a global positioning system unit claim 1 , a camera unit claim 1 , or a radio unit.3. The crop yield estimation system of claim 2 , wherein the environmental sensor unit comprises at least one of an actinometer claim 2 , an ...

Management and display of object-collection data

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

SYSTEM, METHOD, PROGRAM FOR DISPLAY ON WEARABLE TERMINAL

The present invention is to identify the type of the farm product in a view through the wearable terminal and display the expected harvest time on the display of a wearable terminal in augmented reality. The system for displaying the harvest time of a farm product on the display of a wearable terminal, includes: an image acquisition unit that acquires the image of a farm product in a view through the wearable terminal; an identification unit that analyzes the image and identifies the type of the farm product; a selection unit that selects a determination criterion according to the type; a determination unit that analyzes the image based on the determination criterion and determines the color and the size; an estimation unit that estimates the harvest time of the farm product based on the determination result; and a harvest time display unit that displays the estimated harvest time of the farm product seen through the display of the wearable terminal thereon in augmented reality. 1. A system for displaying the harvest time of a farm product on the display of a wearable terminal , comprising:an image acquisition unit that acquires the image of a farm product in a view through the wearable terminal;an identification unit that analyzes the image and identifies the type of the farm product;a selection unit that selects a determination criterion according to the type;a determination unit that analyzes the image based on the determination criterion and determines the color and the size;an estimation unit that estimates the harvest time of the farm product based on the determination result; anda harvest time display unit that displays the estimated harvest time of the farm product seen through the display of the wearable terminal thereon in augmented reality.2. The system according to claim 1 , wherein the identification unit identifies a farm product at the center of a view through the wearable terminal as a target to be identified.3. The system according to claim 1 , ...

DETECTING CROP RELATED ROW FROM IMAGE

System and techniques for detecting a crop related row from an image are described herein. An image that includes several rows where the several rows including crop rows and furrows can be obtained. The image can be segmented to produce a set of image segments. A filter can be shifted across respective segments of the set of image segments to get a set of positions. A line can be fit members of the set of positions, the line representing a crop row or furrow. 1. A device for detecting a crop related row from image , the device comprising:a memory including instructions; and obtain an image that includes several rows, the several rows including crop rows and furrows;', 'segment the image to produce a set of image segments;', 'shift a filter across respective segments of the set of image segments to get a set of positions;', 'fit a line to members of the set of positions; and', 'communicate the line a receiver., 'processing circuitry that is configured by the instructions to2. The device of claim 1 , wherein the image is an intensity image.3. The device of claim 2 , wherein the intensity image is implemented as a grayscale image.4. The device of claim 3 , wherein a crop row is lighter than a furrow.5. The device of claim 1 , wherein segments in the set of image segments are scanlines of the image.6. The device of claim 1 , wherein claim 1 , to shift the filter across respective segments claim 1 , the processing circuitry is configured by the instructions to shift the filter across several segments in parallel.7. The device of claim 1 , wherein claim 1 , to shift the filter across respective segments claim 1 , the processing circuitry is configured by the instructions to use a first period on a first segment and a second period on a second segment.8. The device of claim 7 , wherein the first period and the second period are respectively selected based on a respective position of the first segment and the second segment in the image.9. The device of claim 8 , wherein ...

SYSTEM AND METHOD FOR DETERMINING AN ATTRIBUTE OF A PLANT

A system and method for determining an attribute of a plant may include obtaining, by a camera, an image of a region; producing, by a rangefinder, a measurement of a distance from the rangefinder to a plant in the region; and determining the height of the plant by correlating data in the image with the measurement. 2. The method of claim 1 , wherein ground surface in the region is identified based on the image.3. The method of claim 1 , wherein ground surface in the region is identified based on a reflection of an electromagnetic wave.4. The method of claim 1 , comprising claim 1 , creating a three dimensional (3D) image of the region based on:a set of images obtained by the camera with submillimetric resolution; anda respective set of measurements.5. (canceled)6. The method of claim 1 , comprising claim 1 , producing claim 1 , by the rangefinder claim 1 , a sequence of measurements and selecting from the set a measurement that corresponds to the time of obtaining the image.7. The method of claim 1 , wherein the camera is adapted to obtain the image at a submillimetric high resolution.8. The method of claim 1 , comprising applying forward motion compensation to enable capturing images at sub-millimetric resolution.9. A method of determining a height of a plant claim 1 , the method comprising:directing at least one of a camera and a range finder at a reflection surface and directing the reflection surface at a region;obtaining a set of images of the region;producing, by a rangefinder, a set of distance measurements from the rangefinder to the plant;selecting, based on correlating data in the images with the distance measurements from the rangefinder to the plant, a first distance measurement from the rangefinder to the plant related to a plant in the region and a second distance measurement from the rangefinder to the plant related to a ground surface in the region, and setting a distance threshold to filter out invalid distance measurements of distance from the ...

ADAPTIVE COLOR TRANSFORMATION TO AID COMPUTER VISION

System and techniques for adaptive color transformation to aid computer vision are described herein. Colors from an image are mapped into a multi-dimensional space to create a distribution of colors in the image. A line can be fit to the distribution. Here, the line includes an angle relative to a coordinate system of the multi-dimensional space. A transformation to colors can then be applied to the image based on the angle of the line. The transformation producing a reduced image where a color complexity of the original image is reduced. 1. A device for adaptive color transformation to aid computer vision , the device comprising:a memory including instructions; andprocessing circuitry that is configured by the instructions to:obtain a color image;map colors of the image into a multi-dimensional space to create a distribution of colors in the image, a dimension of the multi-dimensional space corresponding to a component of a color;fit a line to the distribution, the line including an angle relative to a coordinate system of the multi-dimensional space;apply a transformation to colors in the image based on the angle to produce a reduced image, the transformation reducing a color complexity; andcommunicate the reduced image to a receiver.2. The device of claim 1 , wherein the color image is preprocessed from a first color representation that does not include a luminance component of the color to a second color representation that includes a luminance component.3. The device of claim 2 , wherein the first color representation is red-green-blue.4. The device of claim 2 , wherein the second color representation is one of YUV claim 2 , YIQ claim 2 , YDbDr claim 2 , YCbCr claim 2 , YPbPr claim 2 , ICtCp claim 2 , or xvYCC.5. The device of claim 2 , wherein the first color representation or the second color representation includes a depth component.6. The device of claim 1 , wherein claim 1 , to fit the line to the distribution claim 1 , the processing circuitry is arranged ...

INTELLIGENT CROP MAINTENANCE DEVICE WITH INDEPENDENTLY CONTROLLED BLADES

System that automates crop maintenance activities, such as cultivating and weeding, with a device that intelligently and independently controls two blades that drag along either side of a crop row using sensors to repeatedly track the position of the blades and of the plants in the row. Blades may be moved in and out independently using an actuator for each blade to contour closely around the individual plants, even if plants or rows vary in their positions, and even if plant sizes and shapes differ. An illustrative system may use a single camera and a processor per crop row; the processor may analyze camera images to locate plant positions and shapes, to plan blade trajectories, and to control blade actuators. The processor may be able to control blade movement precisely to respond quickly to sensor input on changes in plant positions, shapes, and sizes along the row. 1. An intelligent crop maintenance device with independently controlled blades , comprising: a first blade configured to drag along or below a soil surface as said vehicle moves through said field;', 'a first blade mount coupled to said first blade and said vehicle;', 'a first actuator configured to position said first blade at any position between a first minimum position and a first maximum position wherein said first actuator is coupled to said first blade mount and is configured to move said first blade closer to or further from said row of plants;', 'a second blade configured to drag along or below said soil surface as said vehicle moves through said field;', 'a second blade mount coupled to said second blade and said vehicle; and,', 'a second actuator configured to position said second blade at any position between a second minimum position and a second maximum position wherein said second actuator is coupled to said second blade mount and is configured to move said second blade closer to or further from said row of plants;', said first blade and said second blade are configured to be positioned ...

Determining A Weed Percentage And Agricultural Control Device

The present disclosure relates to agriculture. The teachings thereof may be embodied in methods to determine a percentage of weeds and/or control device. For example, a method for determining a weed percentage in an observation section of a field may include: acquiring actual spectral information using an optical observation section sensor aimed at the observation section; acquiring reference spectral information using an optical reference sensor aimed at a reference section of the same ground area; determining a difference between the actual spectral information and the reference spectral information; and mapping the difference to the weed percentage using predefined mapping.

PLANT IDENTIFICATION USING HETEROGENOUS MULTI-SPECTRAL STEREO IMAGING

A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time. 1. A method for taking a farming action in a field , comprising:accessing a color image corresponding to a first field of view and comprising a plurality of color pixels comprising (i) one or more color pixels representing a plant in the field, and (ii) one or more color pixels for a particular spectral band;accessing a multispectral image corresponding to a second field of view and comprising a plurality of multispectral pixels comprising (i) one or more multispectral pixels representing the plant in the field, and (ii) one or more multispectral pixels for the particular spectral band; identifying an overlapping region between the first field of view and the second field of view;', 'generating stereo depth information from the multispectral pixels and color pixels for the particular spectral band that correspond to the overlapping region; and', 'encoding the stereo depth information into the depth pixels of the depth map;, 'generating a depth map comprising a plurality of depth pixels representing stereo depth information, the plurality of depth pixels including one or more depth pixels representing the plant in the field, byidentifying the plant in the depth pixels of the depth map; andtaking a farming action in the field based on the identified plant in the depth pixels of the depth map.2. The method of claim 1 , wherein a first image sensor is a digital camera with red claim 1 , green claim 1 , and blue color channels claim 1 , and the color pixels include red claim 1 , green claim 1 , and ...

DEVICE, METHOD AND SYSTEM FOR DETERMINING FLIGHT HEIGHT OF UNMANNED AERIAL VEHICLE

A method and system for determining flight height of an unmanned aerial vehicle (UAV) includes a determining device that includes a camera at bottom of the is UAV carried on a three-axis self-stabilizing tripod head; and a carrier phase difference satellite positioning system (CPDSPS), a graphics processing computer (GPC) and a power supply system provided on top of the UAV The GPC is connected with the CPDSPS, the power supply system and the camera, respectively. An attitude and heading reference system (AHRS) is provided at the bottom of the three-axis self-stabilizing tripod head and connected with the GPC The GPC is configured to determine relative height of the UAV from a canopy of farmland surface crops according to position information acquired by the CPDSPS, attitude information acquired by the AHRS and ground orthographic image acquired by the camera, and determine flight height of the UAV according to the relative height. 1. A device for determining the flight height of an unmanned aerial vehicle , comprising:an unmanned aerial vehicle;a three-axis self-stabilizing tripod head;a carrier phase difference satellite positioning system;an attitude and heading reference system;a graphics processing computer; anda power supply system, wherein;a camera at the bottom of the unmanned aerial vehicle is carried on the three-axis self-stabilizing tripod head, and the three-axis self-stabilizing tripod head is used to maintain an optical axis of the camera;the carrier phase difference satellite positioning system, the graphics processing computer and the power supply system are all provided on top of the unmanned aerial vehicle;the graphic processing computer is connected with the carrier phase difference satellite positioning system, the power supply system and the camera, respectively;the attitude and heading reference system is provided at bottom of the three-axis self-stabilizing tripod head and is connected with the graphics processing computer;the graphic ...

PORTABLE SUBSTANCE ANALYSIS BASED ON COMPUTER VISION, SPECTROSCOPY, AND ARTIFICIAL INTELLIGENCE

A portable complete analysis solution that integrates computer vision, spectrometry, and artificial intelligence for providing self-adaptive, real time information and recommendations for objects of interest. The solution has three major key components: (1) a camera enabled mobile device to capture an image of the object, followed by fast computer vision analysis for features and key elements extraction; (2) a portable wireless spectrometer to obtain spectral information of the object at areas of interest, followed by transmission of the data (data from all built in sensors) to the mobile device and the cloud; and (3) a sophisticated cloud based artificial intelligence model to encode the features from images and chemical information from spectral analysis to decode the object of interest. The complete solution provides fast, accurate, and real time analyses that allows users to obtain clear information about objects of interest as well as personalized recommendations based on the information. 1. A method for analyzing an object for one or multiple characteristic properties using a camera enabled device , a multi-wavelength spectrometer , and artificial intelligence comprising the steps of:(a) obtaining an image of the object using a camera enabled device;(b) using computer vision to extract one or more features of the object;(c) using computer vision to identify one or more regions of interest within one or more of the features of the object;(d) scanning the one or more regions of interest of the object using a multi-wavelength spectrometer over a plurality of wavelengths;(e) measuring a spectrophotometric value for each of the wavelengths of step (d) to generate a spectrum of each region of interest of the object;(f) using artificial intelligence to integrate the spectrum generated in step (e) with the features extracted in step (b) with the regions of interest identified in step (c) to generate an analysis of the one or multiple characteristic properties of the ...

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM STORAGE MEDIUM

An information processing device includes a setting unit and a detection unit The setting unit sets, as an investigation range, an image region including a target object in a captured image in which the target object is captured based on information about a feature of the target object. The detection unit performs predetermined processing relating to the target object within the set investigation range. 1. An information processing device comprising:a processor configured to:set, as an investigation range, an image region including a target object to be measured in a captured image in which the target object is captured based on information about a feature of the target object; andperform predetermined processing relating to the target object within the set investigation range.2. The information processing device according to claim 1 , wherein the processor further starts processing of setting the investigation range when input of predetermined information representing processing start is detected.3. The information processing device according to claim 1 , wherein the processor further determines whether the target object is included within an image range of the captured image while moving the image range at predetermined intervals in the captured image claim 1 , the image range is a range to determine whether to be an image region including the target object claim 1 , andwherein the processor further sets, as the investigation range, the image region within the image range determined as including the target object.4. The information processing device according to claim 1 , wherein the processor further calculates a degree of matching between an image within the image range to determine whether to be an image region including the target object and a sample image of the target object given in advance claim 1 , and sets claim 1 , as the investigation range claim 1 , the image region within the image range having the degree of matching equal to or greater than a ...

AI-POWERED AUTONOMOUS PLANT-GROWTH OPTIMIZATION SYSTEM THAT AUTOMATICALLY ADJUSTS INPUT VARIABLES TO YIELD DESIRED HARVEST TRAITS

Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light. 1. A method of monitoring plant growth in an indoor farm , the method comprising:receiving, from an image sensor, raw image data that represents a plant being monitored; anddetecting a growth phase of the plant being monitored based on the received raw image data, wherein the growth phase of the plant being monitored is detected using a neural network that performs computer-based image analysis on the received raw image data.2. The method of claim 1 , wherein the growth phase of the plant being monitored is detected using the neural network by comparing the received raw image data of the plant being monitored against a known growth phase for a plant of the same type as the plant being monitored.3. The method of claim 1 , further comprising detecting plant stress of the plant being monitored based on the detected growth phase.4. The method of claim 3 , wherein a severity of the detected plant stress is determined based on an environmental temperature of the plant being monitored that is received from a ...

NOISE DETECTION METHOD FOR TIME-SERIES VEGETATION INDEX DERIVED FROM REMOTE SENSING IMAGES

A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration. The time series is then converted back to image space to obtain a noise mask and optimized. The present invention can obtain an accurate noise mask and improve reliability of land surface-related applications. 1step 1, time series feature computation; {'br': None, 'i': I=\u2003\u2003(16)'}, 'assume that a study area consists of n images I acquired at different dates {'br': None, 'i': T=\u2003\u2003(17)'}, 'acquisition date T of each image is expressed as'}{'sub': i−1', 'i', 'i−1', 'i, 'for the acquisition dates there is a relationship t≤t, i=2,3, . . . , n, that is, tis prior to t;'} {'br': None, 'i': P={P', ', P', ', . . . , P, 'sup': 1', '2', 'm', 'T, '}\u2003\u2003(18)'}, 'm pixels P in the study area are expressed as{'sup': j', 'j', 'm, 'sub': 'j=1', 'claim-text': {'br': None, 'i': X', '=\u2003\u2003(19)'}, 'TSVI Xextracted from time series images for pixel (P)is expressed ...

SYSTEM AND METHOD FOR DETERMINING FIELD CHARACTERISTICS BASED ON DATA FROM MULTIPLE TYPES OF SENSORS

A system for determining a field characteristic during the performance of an agricultural operation includes a vision-based sensor configured to capture vision data indicative of a field characteristic of the field, as well as a secondary sensor configured to capture secondary data indicative of the field characteristic. The system also includes a controller configured to receive the vision data from the vision-based sensor and secondary data from the secondary sensor for use in determining the field characteristic. The controller is further configured to determine when the received vision data is occluded, and, when it is determined that the vision data is occluded, to determine the field characteristic based on the secondary data. 1. A system for determining field characteristics during the performance of an agricultural operation , the system comprising:an agricultural machine configured to perform an agricultural operation on a field across which the agricultural machine is traveling;a vision-based sensor provided in operative association with the agricultural machine having a field of view of a section of the field, the vision-based sensor configured to capture vision data indicative of a field characteristic of the field;a secondary sensor provided in operative association with the agricultural machine having substantially the same field of view of the section of the field as the vision-based sensor, the secondary sensor configured to capture secondary data indicative of the field characteristic wherein the secondary sensor is a non-vision-based sensor; and receive the vision data from the vision-based sensor and secondary data from the secondary sensor for use in determining the field characteristic;', 'determine whether an object has been detected within the field of view based on the received vision data;', 'responsive to non detection of an object within the field of view based on the received vision data, determine the field characteristic based on the ...

Vehicle Controllers For Agricultural And Industrial Applications

Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes accessing a map data structure storing a map representing locations of physical objects in a geographic area; accessing current point cloud data captured using a distance sensor connected to a vehicle; detecting a crop row based on the current point cloud data; matching the detected crop row with a crop row represented in the map; determining an estimate of a current location of the vehicle based on a current position in relation to the detected crop row; and controlling one or more actuators to cause the vehicle to move from the current location of the vehicle to a target location. 1. A system , comprising:one or more motion sensors configured to detect motion of a vehicle;actuators configured to control motion of the vehicle and to control operation of an implement, wherein the implement is connected to the vehicle and configured to selectively perform an operation in a vicinity of the vehicle; and access a map data structure storing a map representing locations of physical objects in a geographic area;', 'access a path data structure that includes a sequence of waypoints that each specify a position within the map, wherein a waypoint in the sequence of waypoints includes implement control data for operating the implement at a location corresponding to the position of the waypoint;', 'access motion sensor data captured using the one or more motion sensors;', 'determine, based on the motion sensor data, an estimate of a current location of the vehicle;', 'control one or more of the actuators to cause the vehicle to move from the current location of the vehicle to the location corresponding to the position of the waypoint;', 'detect, based on the motion sensor data, when the vehicle arrives at the location corresponding to the position of the waypoint; and', 'responsive to the vehicle arriving at the location corresponding to the ...

Vehicle Controllers For Agricultural And Industrial Applications

Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes accessing a map data structure storing a map representing locations of physical objects in a geographic area; accessing current point cloud data captured using a distance sensor connected to a vehicle; detecting a crop row based on the current point cloud data; matching the detected crop row with a crop row represented in the map; determining an estimate of a current location of the vehicle based on a current position in relation to the detected crop row; and controlling one or more actuators to cause the vehicle to move from the current location of the vehicle to a target location.

VEHICLE CONTROLLERS FOR AGRICULTURAL AND INDUSTRIAL APPLICATIONS

Systems and methods for vehicle controllers for agricultural and industrial applications are described. For example, a method includes receiving image data, captured using one or more image sensors connected to a vehicle, depicting one or more plants in a vicinity of the vehicle; detecting the one or more plants based on the image data; responsive to detecting the one or more plants, adjusting implement control data; and controlling, based on the adjusted implement control data, an implement connected to the vehicle to perform an operation on the one or more plants. 1. A system , comprising:one or more image sensors connected to a vehicle;actuators configured to control operation of an implement, wherein the implement is connected to the vehicle and configured to selectively perform an operation in a vicinity of the vehicle; and receive image data, captured using the one or more image sensors, depicting one or more plants in a vicinity of the vehicle;', 'detect the one or more plants based on the image data;, 'a processing apparatus configured to 'control one or more of the actuators, based on the adjusted implement control data, to cause the implement to perform the operation on the one or more plants.', 'responsive to detecting the one or more plants, adjust implement control data; and'}2. The system of claim 1 , in which the one or more image sensors comprise a normalized difference vegetation index camera connected to the vehicle claim 1 , and in which the processing apparatus is configured to:receive normalized difference vegetation index data, captured using the normalized difference vegetation index camera, for the one or more plants, wherein adjusting the implement control data includes adjusting an amount of input to be deposited on the one or more plants based on the normalized difference vegetation index data.3. The system of claim 1 , in which the implement is configured to selectively deposit input in a vicinity of the vehicle claim 1 , and the ...

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND VEGETATION MANAGEMENT SYSTEM

The present disclosure relates to an information processing apparatus, an information processing method, and a vegetation management system each capable of managing vegetation in such a manner as to maintain a high quality state and achieve uniform growth. 1. An information processing apparatus comprising:an analysis unit that analyzes, as at least input information, an image in which vegetation corresponding to a target of management is captured; anda determination unit that determines work contents to be performed for the vegetation, according to an analysis result output from the analysis unit.2. The information processing apparatus according to claim 1 , further comprising:an information database that stores the input information and information indicating the analysis result, whereinthe determination unit determines the work contents on a basis of information stored in the information database.3. The information processing apparatus according to claim 1 , whereinthe analysis unit divides an entire surface of the vegetation corresponding to the target of management into plural divisional regions and outputs the analysis result for each of the divisional regions.4. The information processing apparatus according to claim 1 , whereinthe analysis unit outputs, as the analysis result, a vegetation index associated with grass that is the vegetation.5. The information processing apparatus according to claim 4 , whereinthe vegetation index includes a grass leaf covering ratio that is a ratio of grass present per unit area and calculated on a basis of the image for each of divisional regions that are plural divisions of a grass surface.6. The information processing apparatus according to claim 4 , whereinthe vegetation index includes a solar radiation amount calculated by integrating luminance values of sunlight reaching the grass surface, on a basis of the image, for each of divisional regions that are plural divisions of a grass surface.7. The information processing ...

INVENTORY, GROWTH, AND RISK PREDICTION USING IMAGE PROCESSING

According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images. 1. An inventory , growth , and risk prediction using image processing system comprising: receive a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path, where the plurality of images include a plurality of objects, and', 'pre-process the plurality of images for feature extraction from the plurality of images;, 'an image pre-processor, executed by at least one hardware processor, to'}a feature extractor, executed by the at least one hardware processor, to extract a plurality of features of the plurality of objects from the plurality of pre-processed images by using a combination of computer vision techniques;an object level parameter generator, executed by the at least one hardware processor, to determine at least one parameter related to the plurality of objects from the plurality of extracted features; and a visual indication of density of distribution of the plurality of objects related to a portion of at least one of the plurality of images, and', 'an alert corresponding to the plurality of objects related to the portion of the at least one of the plurality of images., 'a partition level output ...

Image Space Motion Planning Of An Autonomous Vehicle

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Image Space Motion Planning Of An Autonomous Vehicle

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives. 1. A system comprising:a processing unit; and receive an image of a physical environment;', 'wherein the cost value associated with each of the plurality of regions of the image is indicative of a level of risk associated with navigating in an area of the physical environment corresponding to the region of the image;', 'process the image to generate a cost function map corresponding to an image space of the image, the cost function map associating a cost value with each of a plurality of regions of the image,'}, 'identify, based on the cost function map, a particular region of the image that is associated with a higher cost value than one or more of the other regions of the image; and', 'optimize a planned 3D trajectory for an autonomous vehicle through the physical environment to lower an overall cost of the planned 3D trajectory by adjusting the planned 3D trajectory such that a projection of the planned 3D trajectory in the image space of the image avoids the particular region of the image., 'a memory unit coupled to the processing unit, the memory unit including instructions stored thereon, which when ...

METHOD AND APPARATUS FOR RADIOMETRIC CALIBRATION AND MOSAICKING OF AERIAL IMAGES

The present invention relates to a system for performing radiometric calibration of mosaicking of images. The system includes a calibration reference positioned about an area to be imaged. A sensor is disposed on an aerial vehicle in flight over the area to be imaged. A processor is in communication with the sensor. A plurality of images are obtained by the sensor and are radiometrically calibrated and mosaicked by the processor regardless of whether a calibration reference is visible in an individual image of the plurality of images. 1. A system for performing radiometric calibration and mosaicking of images , the system comprising:a calibration reference positioned about an area to be imaged;a sensor disposed on an aerial vehicle in flight over the area to be imaged;a processor in communication with the sensor; andwherein a plurality of images obtained by the sensor and transmitted to the processor; andwherein the processor automatically mosaicks and radiometrically calibrates the images obtained by the sensor.2. The system of claim 1 , wherein the aerial vehicle is manned.3. The system of claim 1 , wherein the aerial vehicle is an unmanned aerial vehicle.4. The system of claim 1 , wherein the calibration reference is mobile.5. The system of claim 1 , wherein the calibration reference comprises a self-cleaning coating.6. The system of claim 1 , wherein the calibration reference comprises an upper calibration target and a lower calibration target.7. The system of claim 1 , wherein the sensor comprises a camera.8. The system of claim 1 , wherein aerial vehicle comprises a global positioning (“GPS”) receiver.9. The system of claim 1 , wherein the sensor is configured to measure reflectance in the visible and near infra-red spectrums.10. The system of claim 1 , wherein the sensor is configured to detect thermal energy.11. A system for performing radiometric calibration and mosaicking of images claim 1 , the system comprising:a mobile calibration reference positioned ...

METHOD AND APPARATUS FOR MEASURING PLANT TRICHOMES

Systems and methods analyze a plant comprising trichomes. The system can include a back-end computer system communicably couplable to an imaging device. The imaging device captures multiple images of the plant at a plurality of different focal distances, combines the multiple images into a composite image having a greater depth of field utilizing focal stacking, and transmits the composite image to the back-end computer system. The back-end computer system identifies the trichomes imaged within the composite image utilizing a machine learning system, determines a property of the identified trichomes, determines a projected harvest time for the plant according to the property, and provides the projected harvest time to a user. The trichomes within the composite image can be identified utilizing a machine learning system. 1. A computer-implemented method of analyzing a plant comprising trichomes , the method comprising:receiving, by a computer system, a plurality of images of the plant at a plurality of different focal distances;combining, by the computer system, the plurality of images into a composite image having a greater depth of field utilizing focal stacking;identifying, by the computer system, the trichomes imaged within the composite image utilizing a machine learning system;determining, by the computer system, a property of the identified trichomes utilizing the machine learning system;determining, by the computer system, a projected harvest time for the plant according to the property; andproviding, by the computer system, the projected harvest time to a user.2. The method of claim 1 , wherein identifying the trichomes comprises identifying a trichome type from a plurality of trichome types in which each of the trichomes is classified.3. The method of claim 2 , wherein the plurality of trichome types comprises clear claim 2 , cloudy claim 2 , and amber.4. The method of claim 1 , wherein the property comprises a count of the trichomes.5. The method of claim ...

FORESTRY MANAGEMENT TOOL FOR ASSESSING RISK OF CATASTROPHIC TREE FAILURE DUE TO WEATHER EVENTS

Systems, apparatuses and methods for determining a risk of catastrophic failure for a tree based on a mechanistic model of physical characteristics of the tree are described. The systems, apparatuses and methods include a mobile sensing platform comprising one or more sensors for obtaining data, a transport vehicle configured to transport the mobile sensing platform, a positioning sensor configured to precisely calculate geographic coordinates of the positioning sensor and the location of the positioning sensor relative to a reference object as positioning data, and a processor configured to fuse the imaging data and the positioning data in order to determine conditions for catastrophic tree failure. 1. A system capable of determining a risk of catastrophic failure for a tree based on a mechanistic model of physical characteristics of the tree , the system comprising:a mobile sensing platform comprising one or more sensors for obtaining imaging data;a transport vehicle configured to transport the mobile sensing platform;a positioning sensor configured to precisely calculate geographic coordinates of the positioning sensor and the location of the positioning sensor relative to a reference object as positioning data; anda processor configured to fuse the imaging data and the positioning data in order to determine conditions for catastrophic tree failure.2. The system of claim 1 , wherein the one or more sensors may include a LiDAR sensor claim 1 , an RGB sensor claim 1 , a multispectral imaging sensor claim 1 , a hyperspectral imaging sensor claim 1 , a soil composition sensor claim 1 , an ultrasonic sensor claim 1 , and/or a sonar imaging sensor.3. The system of claim 1 , wherein the positioning sensor achieves centimeter level accuracy.4. The system of claim 1 , wherein the positioning sensor includes an inertial measurement unit that calculates the change in location and orientation relative to the former position and orientation of the positioning sensor based on ...

SYSTEM AND METHOD FOR EARTH OBSERVATION AND ANALYSIS

A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest. 1. A process for Earth observation and analysis , the process comprising:i) pre-processing remote sensing images to filter said images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process;ii) applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values, through a regression (or classification) process in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process;iii) applying the output from step ii) to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring; andiv) generating calibrated output images corresponding to the specific regions defining the Areas of Interest.2. A process as ...

SYSTEM AND METHOD FOR REMOTE NITROGEN MONITORING AND PRESCRIPTION

A method and system for managing nitrogen applied by nitrogen application equipment to a geographic region includes determining a growth stage for the geographic region using a crop module, and determining a nitrogen change for the geographic region based on the growth stage using a nitrogen change module, which can additionally or alternatively include determining an amount of nitrogen initially available for a geographic region. 1a) receiving a first image of the geographic region, the first image associated with a first time;b) determining a first growth stage at the first time for plants within the geographic region based on the first image using a first crop module;c) determining a first nitrogen change for the geographic region based on the first growth stage using a nitrogen change module;d) estimating a first nitrogen availability for the geographic region at the first time based on the first nitrogen change;e) repeating b) to d) for a second nitrogen availability for the geographic region at a second time based on a second nitrogen change and the first nitrogen availability;f) determining a nitrogen prescription for the geographic region based on the second nitrogen availability using a nitrogen prescription module; andg) generating first control instructions based on the nitrogen prescription, wherein nitrogen application equipment applies nitrogen according to the first control instructions.. A method for managing nitrogen within a geographic region, the method comprising: This application is a continuation of U.S. application Ser. No. 16/039,170, filed 18 Jul. 2018, which is a continuation of U.S. application Ser. No. 15/483,062, filed 10 Apr. 2017, which is a continuation of U.S. application Ser. No. 15/345,027, filed 7 Nov. 2016, which claims the benefit of U.S. Provisional Application No. 62/252,102 filed 6 Nov. 2015, both of which are incorporated in their entirety by this reference.U.S. application Ser. No. 15/345,027, filed 7 Nov. 2016, which is ...

System and Method for Identifying a Number of Insects in a Horticultural Area

A system comprises a digital camera to capture images of a horticultural area, a device processor and a non-transitory computer readable medium storing instructions executable by the device processor to capture, using the digital camera, a first digital image of a horticultural area containing an insect trap, isolate a portion of the first digital image using the trap detection parameters, the portion of the first digital image corresponding to the insect trap, perform automated particle detection on the portion of the first digital image according to the insect detection parameters to identify regions of pixels in the portion of the first digital image that have the insect recognition color and that pass filter criteria, determine a cardinality of insects on the first object based on a number of identified regions of pixels, store the cardinality of insects in association with the first digital image and provide the cardinality of insects for display in a graphical user interface. 1. A system comprising:a digital camera;a device processor;a data store storing trap detection parameters selected to identify pixels corresponding to insect traps and particle detection parameters selected to detect pixels corresponding to insects, the insect detection parameters including an insect recognition color and filter criteria; and capture, using the digital camera, a first digital image of a horticultural area containing an insect trap;', 'isolate a portion of the first digital image using the trap detection parameters, the portion of the first digital image corresponding to the insect trap;', 'perform automated particle detection on the portion of the first digital image according to the insect detection parameters to identify regions of pixels in the portion of the first digital image that have the insect recognition color and that pass the filter criteria;', 'determine a cardinality of insects on the first object based on a number of identified regions of pixels;', 'store ...

Method of Crop Analysis using Drone with Flying and Driving Capability

There is provided method of analyzing a field of crops using a drone. The method initially includes remotely controlling the drone to fly the drone to adjacent the field of crops. The method further includes remotely controlling the drone to land the drone adjacent the crops. The method further includes remotely controlling the drone to drive the drone on the ground along the crops. The method further includes capturing ground-based images of the crops at an upward view angle using a multispectral camera on-board the drone. 1. A method of analyzing a field of crops using a drone , the method comprising the steps of:a) remotely controlling the drone to fly the drone to adjacent the field of crops;b) remotely controlling the drone to land the drone adjacent the crops;c) remotely controlling the drone to drive the drone on the ground along the crops; andd) capturing ground-based images of the crops at an upward view angle using a multispectral camera on-board the drone.2. The method of wherein step a) further includes capturing flight images of the field of crops claim 1 , while flying claim 1 , using the multispectral camera.3. The method of wherein the captured flight images are based upon visible light images.4. The method of wherein the captured flight images are based upon infrared images.5. The method of wherein step a) further includes producing a Normalized Difference Vegetation Index (NDVI) map using the captured flight images.6. The method of wherein step b) further includes remotely controlling the drone to land the drone adjacent the crops at a location based upon the NDVI map.7. The method of wherein step a) further includes transmitting the captured flight images.8. The method of wherein step d) includes rotating the multispectral camera to an upward view angle.9. The method of wherein the captured ground-based images are based upon visible light images.10. The method of wherein the captured ground-based images are based upon thermal images.11. The method ...

GENERATING RADIOMETRICALLY CORRECTED SURFACE IMAGES

A system and method for generating radiometrically corrected surface images is provided. This includes providing one or more surface images of a first resolution of a surface area in the form of digital images which has a surface image sensor measurement of an intensity value of radiation in a given wavelength band reflected from the surface for each pixel of the images. A reference image of a second resolution of a corresponding surface area is provided to the surface of the surface image. The reference image has a surface reflectance for each pixel of the reference image for an equivalent wavelength band to the given wavelength band of the surface image. A functional relationship is modelled which relates the surface image sensor measurement for pixels of a surface image to the surface reflectance for pixels of the reference image to provide an estimated surface reflectance for each pixel of the surface images. 1. A method for generating radiometrically corrected surface images comprising:providing one or more surface images of a first resolution of a surface area in the form of one or more digital images having a surface image sensor measurement (DN) of an intensity value of radiation in a given wavelength band reflected from the surface for each pixel of the images;{'sub': 't', 'providing a reference image of a second resolution of a corresponding surface area to the surface area of the one or more surface images, wherein the reference image has surface reflectance (ρ) for each pixel of the reference image for an equivalent wavelength band to the given wavelength band of the one or more surface images; and'}{'sub': 't', 'modelling a functional relationship that relates the surface image sensor measurement for pixels of a surface image to the surface reflectance (ρ) for pixels of the reference image to provide an estimated surface reflectance for each pixel of the surface images.'}2. The method as claimed in claim 1 , wherein modelling the functional relationship ...

METHOD OF PREDICTING CROP YIELD LOSS DUE TO N-DEFICIENCY

A method for determining the yield loss of a crop using remote sensor data is described. The yield loss is determined using the reflectivity of light by the crop canopy measured from remote sensor data such as an aerial photograph that is digitized and spatially referenced to the field's longitude and latitude. Pixel values from the aerial photograph, expressed relative to pixel values from well fertilized areas of the field, are transformed to yield losses using a linear transformation that was developed using empirical data. A similar method is described to determine recommended nitrogen fertilization rates for the crop fields. The yield loss data is useful for nitrogen fertilization management, as it allows a producer of crops to weigh the expense of fertilization against the loss of revenue due to yield loss. 1. A method for determining a crop yield loss due to nitrogen deficiency in at least one or more crop fields based on an analysis of at least one or more remote sensing images , the method comprising:a. obtaining the one or more remote sensing images of the at least one or more crop fields;b. assigning spatial coordinates to the at least one or more remote sensing images, represented by pixels or polygons comprised of aggregated pixels, from the at least one or more remote sensing images;c. calculating a combination index value comprising at least two wavelengths at each pixel;d. selecting a reference index value from the combination index values;e. calculating a relative index value of each pixel in the at least one or more remote sensing images by dividing the combination index value by the reference index value;f. estimating a yield loss of each pixel from a function of the relative index values; and,g. constructing a yield loss map from a spatially referenced yield loss of each pixel; wherein the yield loss comprises a decrease in crop yield per acre of a crop.2. The method of claim 1 , wherein the yield loss (YL) of each pixel is estimated by ...

METHODS AND SYSTEMS FOR ANALYZING A FIELD

Methods and systems for analyzing a field. The methods and systems acquire a thermal image indicative of thermal energy emitted by the soil and/or plants in the field and process the thermal image to assess variations in certain characteristics of the soil and/or plants. 1: A method of analyzing a field , the method comprising:positioning an imaging system in an aircraft;acquiring with the imaging system a first aerial thermal image of at least a portion of the field that contains plants while the aircraft is in flight over the field, wherein the aerial thermal image comprises pixels indicating different levels of thermal energy emitted by the plants within the portion of the field while the aircraft is in flight over the field;processing the first aerial thermal image to assess relative variations in temperatures among the plants within the portion of the field by assessing variations in the pixels of the first aerial thermal image;acquiring with the imaging system a second aerial thermal image of the portion of the field that is indicative of the thermal energy emitted by the plants within the portion of the field while the aircraft is in flight over the field at a time after the first aerial thermal image is acquired;processing the second aerial thermal image to assess variations in temperatures among the plants within the portion of the field by assessing variations in the pixels of the aerial thermal image; andcomparing the first aerial thermal image with the second aerial thermal image to assess the health or growth of the plants over a time period between when the first aerial thermal image was acquired and the time when the second aerial thermal image was acquired.2: The method of claim 1 , wherein the variations in temperatures among the plants are assessed to detect disease in the plants.3. The method of claim 1 , wherein the variations in temperatures among the plants are assessed to detect insect infestation in the plants.4: The method of claim 1 , ...

Work layer imaging and analysis for implement monitoring, control and operator feedback

A soil imaging system having a work layer sensor disposed on an agricultural implement to generate an electromagnetic field through a soil area of interest as the agricultural implement traverses a field. A monitor in communication with the work layer sensor is adapted to generate a work layer image of the soil layer of interest based on the generated electromagnetic field. The work layer sensor may also generate a reference image by generating an electromagnetic field through undisturbed soil. The monitor may compare at least one characteristic of the reference image with at least one characteristic of the work layer image to generate a characterized image of the work layer of interest. The monitor may display operator feedback and may effect operational control of the agricultural implement based on the characterized image.

SYSTEM AND METHOD FOR DETERMINING FIELD CHARACTERISTICS BASED ON A DISPLAYED LIGHT PATTERN

In one aspect, a system for determining field characteristics during the performance of an agricultural operation may include an imaging device configured to capture image data associated with a portion of the field within a field of view of the imaging device. The system may also include an illumination device configured to emit a light directed at the portion of the field within the field of view of the imaging device. Furthermore, a controller of the system may be configured to control an operation of the illumination device such that a light pattern is displayed on a field surface of the field. Moreover, the controller may be configured to receive image data indicative of the displayed light pattern from the imaging device. Additionally, the controller may be configured to determine a field characteristic of the field based on the displayed light pattern. 1. A system for determining field characteristics during the performance of an agricultural operation , the system comprising:an agricultural machine configured to perform an agricultural operation on a field across which the agricultural machine is traveling;an imaging device provided in operative association with the agricultural machine, the imaging device configured to capture image data associated with a portion of the field within a field of view of the imaging device;an illumination device provided in operative association with the agricultural machine, the illumination device configured to emit a light directed at the portion of the field within the field of view of the imaging device; and control an operation of the illumination device such that a light pattern is displayed on a field surface of the field;', 'receive image data indicative of the displayed light pattern from the imaging device; and', 'determine a field characteristic of the field based on the displayed light pattern., 'a controller communicatively coupled to the imaging device and the illumination device, the controller configured to2. ...

APPARATUS FOR PROVIDING OPTIMAL PEST CONTROL RECIPE DEPENDING ON PROGRESSION OF DISEASE AND PEST DAMAGE, AND METHOD THEREOF

An apparatus for providing an optimal pest control recipe depending on a progress of disease and pest damages and a method thereof are provided. The apparatus for providing an optimal pest control recipe depending on a progress of disease and pest damages, in which the apparatus is connected with an photographing device and a user terminal, may include: an input module configured to receive image data of crops from the photographing device; a diagnosis module configured to analyze the image data to determine a type and progress of the disease and pest damages; a prescription module configured to generate a pest control recipe according to the determined progress of the disease and pest damages; and a communication module configured to transmit the pest control recipe to the user terminal.

SYSTEMS, DEVICES, AND METHODS FOR MONITORING AND ASSESSING CHARACTERISTICS OF HARVESTED SPECIALTY CROPS

A system for use in connection with assessing characteristics of harvested specialty crops, the system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining an image of a set of harvested specialty crops and associated depth information; generating a 3D surface model of the set of harvested specialty crops using the image and the depth information; and estimating the volume of the set of harvested specialty crops using the 3D surface model. 1. A system for use in connection with assessing characteristics of harvested specialty crops , the system comprising:at least one computer hardware processor; and obtaining an image of a set of harvested specialty crops and associated depth information;', 'generating a 3D surface model of the set of harvested specialty crops using the image and the depth information; and', 'estimating the volume of the set of harvested specialty crops using the 3D surface model., 'at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform2. The system of claim 1 , wherein the 3D surface model comprises a 3D mesh.3. The system of claim 1 , wherein estimating the volume of the set of harvested specialty crops comprises computing a volume integral using the 3D surface model.4. The system of claim 3 , wherein the 3D surface model comprises a plurality of sections claim 3 , and wherein computing the volume integral comprises:estimating, using dimensions and depth information for each section of the 3D surface model, a sectional volume of harvested specialty crops in each section, to obtain a plurality of sectional volumes; ...

METHODS FOR AGRONOMIC AND AGRICULTURAL MONITORING USING UNMANNED AERIAL SYSTEMS

A method for agronomic and agricultural monitoring includes designating an area for imaging, determining a flight path above the designated area, operating an unmanned aerial vehicle (UAV) along the flight path, acquiring images of the area using a camera system attached to the UAV, and processing the acquired images. 1. A method for agronomic and agricultural monitoring , the method comprising:using a first central processing unit (CPU) at a base station, monitoring an unmanned aerial vehicle (UAV), as the UAV flies along a flight path above an area and as the UAV performs:using a second CPU of the UAV, capturing a plurality of initial images of the area as the UAV flies along the flight path;using the second CPU of the UAV, receiving an identification of one or more target areas for taking additional different images;using the second CPU of the UAV, in response to receiving the identification: causing the UAV to capture the one or more additional different images of the one or more target areas;transmitting the plurality of initial images and the one or more additional different images to an image recipient.2. The method of claim 1 , further comprising the UAV identifying itself the one or more target areas for which the one or more different images are required as the UAV orthorectifies the plurality of initial images and identifies areas with a low quality imagery.3. The method of claim 1 , further comprising:using the UAV, sending to a computing device an indication of areas having certain characteristics comprising one or more of low nitrogen levels, low crop maturity levels, or high crop shadow levels;receiving, from the computing device, instructions to acquire one or more images of the one or more target areas for which the one or more additional different images are required;receiving, from the computing device, an identification of the one or more target areas for which the one or more additional different images are required.4. The method of claim 1 , ...

AGRICULTURAL ROBOT SYSTEM

The present invention relates to an agricultural robot system having a laser beam generator to cut peduncles or thin blossoms and, more particularly, to an agricultural robot system for recognizing fruits, flowers, or flower buds from camera input images and then scanning laser beams to cut peduncles or thin blossoms (burn or cure flowers or flower buds), thereby speeding up the operation, and the non-contact operation prevents damage to crops and contamination by viruses and germs and saves on labor. 1. An agricultural robot system , comprising:a laser beam generator provided to generate a laser beam;a galvano-scanner configured to perform a peduncle cutting or blossom thinning work by radiating the laser beam from the laser beam generator;a camera which is installed facing a scanning surface of the galvano-scanner and is able to take pictures of fruits or flowers;an image collection unit which is able to take pictures of images containing the fruits or flowers from the camera;an image recognition unit which is able to recognize the fruits, peduncles and flowers from the images taken by the image collection unit; anda target region detection unit which is able to detect a peduncle cutting or blossom thinning target region from the image recognition information of the image recognition unit, wherein the laser beam scans the target region using the galvano-scanner based on the target region information detected by the target region detection unit, thus performing a peduncle cutting or blossom thinning work or a peduncle cutting and blossom thinning work.2. The system of claim 1 , wherein there are provided a cartesian robot or an articulated robot or a delta robot instead of the galvano-scanner. The present invention relates to an agricultural robot system, and in particular to an agricultural robot system which makes it possible to more quickly and sanitarily cut peduncles or thin blossoms (burn or cure flowers or flower buds) in such a way to radiate a laser beam ...

IMAGE SPACE MOTION PLANNING OF AN AUTONOMOUS VEHICLE

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives. 1. A method for planning the motion of an autonomous vehicle through a physical environment based on an image space analysis of captured images of the physical environment , the method comprising:receiving, by a computer system, an image of the physical environment, the image captured by an image capture device coupled to the autonomous vehicle;processing, by the computer system, the received image to identify a region of the image associated with a particular property;projecting, by the computer system, a predicted three-dimensional (3D) trajectory of the autonomous vehicle through the physical environment into an image space of the received image;generating, by the computer system, a planned 3D trajectory of the autonomous vehicle through the physical environment based at least in part on an image space analysis of the relationship between the projection of the predicted 3D trajectory and the identified region of the image.2. The method of claim 1 , wherein the particular property of the identified region of the image is a level of uncertainty in estimated depth to objects depicted in the image.3. The ...

Spray characterization by optical image analysis

Optical imaging systems and methods are disclosed herein for investigating and analyzing one or more characteristics of a spray, including spray front velocity, spray sheet length, spray break-up dynamics, drift potential, and drop-size distribution. Optical imaging systems disclosed herein include an image acquisition device, one or more illumination source, a processor for processing and analyzing image data, a background, and a spray system that includes at least a nozzle. The image acquisition device and illumination sources are positioned on the same side of the spray. The processor has a first algorithm to generate a feedback correction factor and a second algorithm to identify droplets and develop a drop-size distribution for the spray. Optical imaging methods for determining spray characteristics are also disclosed and generally include the steps of providing an imaging system, capturing images of the spray with the imaging system, processing the images, and analyzing the images.

OBJECT TRACKING ACROSS MULTIPLE IMAGES

Implementations are described herein for utilizing various image processing techniques to facilitate tracking and/or counting of plant-parts-of-interest among crops. In various implementations, a sequence of digital images of a plant captured by a vision sensor while the vision sensor is moved relative to the plant may be obtained. A first digital image and a second digital image of the sequence may be analyzed to determine one or more constituent similarity scores between plant-parts-of-interest across the first and second digital images. The constituent similarity scores may be used, e.g., collectively as a composite similarity score, to determine whether a depiction of a plant-part-of-interest in the first digital images matches a depiction of a plant-part-of-interest in the second digital image. 1. A method implemented by one or more processors , comprising:obtaining a sequence of digital images of a plant captured by a vision sensor while the vision sensor is moved relative to the plant;analyzing a first digital image and a second digital image of the sequence to determine one or more measures of pixel movement across the first and second digital images;based on the one or more measures of pixel movement, predicting a first set of one or more bounding shapes surrounding one or more respective plant parts depicted in the second digital image;analyzing the second digital image of the sequence to detect a second set of one or more ground truth bounding shapes surrounding the one or more respective plant parts depicted in the second digital image; andbased on a comparison of the first and second sets of bounding shapes, matching one or more of the respective plant parts depicted in the second digital image with one or more respective plant parts depicted in the first digital image.2. The method of claim 1 , further comprising analyzing the first digital image of the sequence to identify an original set of one or more ground truth bounding shapes surrounding the one ...

Plant Group Identification

A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model. 1. A method for treating a plant in a field by a farming machine that moves through the field , the farming machine including a plurality of treatment mechanisms:receiving information describing a plurality of weed groups to be identified in the field by a plant identification model, each weed group comprising one or more weeds;accessing an image of the field captured by an image sensor, the image comprising a group of pixels representing the plant; determine, based on the group of pixels representing the plant, that the plant is a weed in a weed group;', 'classify the group of pixels representing the plant as the weed group; and', 'determine a representative location of the classified group of pixels in the image;, 'applying the plant identification model to the image, the plant identification model configured togenerating a plant treatment instruction for treating the plant with a treatment mechanism of the plurality of treatment mechanisms based on the labeled weed group and the representative location; andactuating the plant treatment mechanism using the plant treatment instruction such that the plant is treated with the plant ...

Plant Cultivation

The invention provides a plant cultivation system and a method of cultivating plants. The system includes plant sensors in the form of image sensors, arranged to capture digital plant images; processing hardware including a processor, a data storage facility in communication with the processor and input/output interfaces connectable to the plant sensors and in communication with the processor, the hardware being configured to implement a convolutional neural network (CNN) trained from a library of plant images to recognize predefined plant conditions from the digital plant images captured by the image sensors and to provide a matching score of a plant image when compared to the predefined plant conditions with which the CNN has been trained; and a reference library containing treatment regimes associated with predefined plant conditions, the output interface of the processing hardware arranged to present the predefined plant condition and associated treatment regime to a user. 1. A plant cultivation system , which comprisesplant sensors in the form of image sensors, arranged to capture digital plant images;processing hardware comprising a processor, a data storage facility in communication with the processor and input/output interfaces connectable to the plant sensors and in communication with the processor, the hardware being configured to implement a convolutional neural network (CNN) trained from a library of plant images to recognize predefined plant conditions from the digital plant images captured by the image sensors and to provide a matching score of a plant image when compared to the predefined plant conditions with which the CNN has been trained; anda reference library containing treatment regimes associated with predefined plant conditions, the output interface of the processing hardware arranged to present the predefined plant condition and associated treatment regime to a user.2. The plant cultivation system of claim 1 , in which the image sensors are ...

A WEEDING SYSTEM AND METHOD FOR USE IN AGRICULTURE

A weeding system for an agricultural weeding vehicle including at least one camera for mounting on an agricultural vehicle to acquire an image of a portion of a field while the agricultural vehicle is moving. The system also includes a spraying unit to be mounted on the agricultural vehicle, a control unit to receive images from the camera, generate a weed species detection signal and selectively cause the spraying of a chemical agent by the spraying unit on the basis of the weed species detection signal. The control unit executes a training-based algorithm based on a set of reference images labeled to indicate the presence or not of at least one weed species in the images. 1. A weeding system for an agricultural weeding vehicle , comprising:at least one camera adapted to be mounted on an agricultural vehicle to acquire an image of a portion of a field while said agricultural vehicle is travelling in said field, said image comprising a matrix of pixel values,a spraying unit adapted to be mounted on said agricultural vehicle,a control unit adapted to receive images from said camera, generate a weed species detection signal and selectively cause the spraying of a chemical agent by the spraying unit on the basis of said weed species detection signal,the control unit being adapted to execute in a running mode a training-based algorithm, the training being based on a set of reference images labeled so as to indicate the effective presence or not of at least one weed species in said images,wherein each of said reference images comprises a pair of a so-called nighttime image and a so-called daytime image of the same scene, in that said labeling is performed on the daytime images and applied to the corresponding nighttime images, in that the training is based on the labeled nighttime images, and in that in the running mode, the algorithm receives nighttime real images.2. A system according to claim 1 , wherein a species contouring is performed on the daytime images and ...

METHOD AND DEVICE FOR MONITORING COMPREHENSIVE GROWTH OF POTTED LETTUCE

An apparatus for monitoring comprehensive growth condition of potted lettuce consists of a polarized hyperspectral imaging system and a 3D laser scanning system. The polarized hyperspectral imaging system includes a control system, a dual-coordinate sample holder, an image acquisition system, and a light source system, and the polarized hyperspectral imaging system detects water and fertilizer stress in lettuce by polarized hyperspectral multi-dimensional reflection imaging of characteristics on a canopy/leaf scale. The 3D laser scanning system detects morphological characteristics such as the biomass, stem diameter, plant height, and leaf area of lettuce. The method includes: step 1: culturing samples: cultivating the potted lettuce under a water stress, and under a nitrogen stress and water stress; step 2: acquiring morphological data of an entire lettuce plant; step 3: processing and analyzing data; step 4: correcting a model by using a water compensation factor; step 5: establishing a multi-characteristic integration model. 1. A method for monitoring a comprehensive growth condition of a potted lettuce , comprising the following steps:step 1: culturing samples: cultivating the potted lettuce under a water stress, and cultivating the potted lettuce under a nitrogen stress and the water stress; [ 1) before scanning is performed, bonding black-contour high-reflectivity target dots with a diameter of 6 mm on a leaf and a pot of a crop to be scanned, a shortest distance between two of the black-contour high-reflectivity target dots being controlled to be 15 mm; and', '2) operating the 3D laser scanner, measuring a calibration board by using the 3D laser scanner to correct parameters of a sensor, and sequentially obtaining 3D data of all crop samples in a handheld scanning mode; and, '2.1 acquiring 3D laser scanning imaging data by using a handheld, a self-positioning, a 3D laser scanner according to the following steps, 1) placing a sample on a displacement stage of ...

SENSOR ASSEMBLY FOR AN AGRICULTURAL IMPLEMENT AND RELATED SYSTEMS AND METHODS FOR MONITORING FIELD SURFACE CONDITIONS

A system for monitoring field surface conditions includes a support arm and a housing coupled to the support arm such that the housing is supported adjacent to a surface of a field, with the housing extending over a portion of the surface such that a shielded surface area is defined underneath the housing. The system also includes a light source configured to illuminate at least a portion of the shielded surface area defined underneath the housing such that a shadow is created adjacent a surface feature positioned within the shielded surface area and an imaging device configured to capture an image of the surface feature and the adjacent shadow created by the surface feature. Moreover, the system includes a controller configured to estimate a parameter associated with the surface feature based at least in part on an analysis of the adjacent shadow depicted within the image. 1. A system for monitoring field surface conditions during the performance of an agricultural operation , the system comprising:a support arm configured to be coupled to a frame of an agricultural implement;a housing coupled to the support arm such that the housing is supported adjacent to a surface of a field, the housing extending over a portion of the surface such that a shielded surface area is defined underneath the housing across said portion of the surface;a light source supported relative to the housing, the light source configured to illuminate at least a portion of the shielded surface area defined underneath the housing such that a shadow is created adjacent a surface feature positioned within the shielded surface area due to light from the light source being blocked by the surface feature, wherein the light source is configured to direct light across the at least a portion of the shielded surface area at a lighting angle;an imaging device positioned within the housing such that the imaging device has a field of view directed towards the at least a portion of the shielded surface area, ...

Biological Material Fouling Assessment Systems and Methods

Biological material fouling assessment systems and methods are described. According to one aspect, a biological material fouling assessment system includes processing circuitry configured to access image data of an image of a surface of a substrate which has been fouled with biological material, wherein the image data comprises intensity information regarding a plurality of pixels of the image, and wherein the processing circuitry is further configured to process the intensity information regarding the pixels of the image to generate information which is indicative of an amount of the fouling of the biological material upon the surface of the substrate. 1. A biological material fouling assessment system comprising:processing circuitry configured to access image data of an image of a surface of a substrate which has been fouled with biological material, wherein the image data comprises intensity information regarding a plurality of pixels of the image, and wherein the processing circuitry is further configured to process the intensity information regarding the pixels of the image to generate information which is indicative of an amount of the fouling of the biological material upon the surface of the substrate.2. The system of wherein the image is a color image and the processing circuitry is configured to process the intensity information including separating the pixels into respective ones of a plurality of different color channels according to the colors of the pixels claim 1 , and wherein the processing circuitry is configured to process the pixels for the different color channels separately to generate the information which is indicative of the amount of the fouling comprising a value for each of the color channels which is indicative of the amount of the fouling of the biological material upon the surface of the substrate.3. The system of wherein the processing circuitry is configured to process the intensity information including using the intensity ...

DETERMINING RISK POSED BY VEGETATION

The present invention relates to a method of determining the risk to an item posed by vegetation, the method comprising: receiving passive high altitude data, such as satellite imagery, relating to vegetation around the item; determining at least one vegetation feature based on said data; determining a risk posed to the item based on said at least one vegetation feature; and outputting said risk to a user. The invention also relates to a corresponding system and computer program product. 1. A method of determining the risk to an item posed by vegetation , the method comprising:receiving passive high altitude data, such as satellite imagery, relating to vegetation around the item;determining at least one vegetation feature based on said data;determining a risk posed to the item based on said at least one vegetation feature; andoutputting said risk to a user.2. A method according to any preceding claim wherein said passive high altitude data is collected from an altitude of at least 10 km.3. A method according to any preceding claim wherein said passive high altitude data is collected from an altitude of at least 15 km.4. A method according to any preceding claim wherein said passive high altitude data is collected from an altitude of at least 500 km.5. A method according to any of to wherein said passive high altitude data is collected from an altitude of between 160 km to 2 ,000 km.6. A method according to any preceding claim wherein said passive high altitude data comprises satellite imagery.7. A method according to any preceding claim wherein said passive high altitude data comprises multiple images of the vegetation around the item , said multiple images being taken from different angles.8. A method according to wherein said passive high altitude data comprises stereo satellite imagery.9. A method according to any preceding claim wherein said passive high altitude comprises multispectral imagery.10. A method according to any preceding claim wherein said passive ...

WALL LANDSCAPING SYSTEM FOR EASY GROWTH MANAGEMENT WITH AUTOMATIC WATERING AND PLANT GROWTH ANALYSIS

Disclosed is a wall landscaping system for easy growth management with automatic watering and plant growth analysis, which creates a vertical garden on an interior or exterior wall of a structure, the system including: a main body () installed to cover the wall (W) to have a plant grown thereon; a watering part () disposed at an upper end of the main body () to supply water to the plant; a detector () disposed around the main body () to photograph the plant; and a controller () automatically checking and recording a growth state of the plant by analyzing an image photographed by the detector (), and remotely notifying a manager according to the growth state of the plant, whereby it is possible to automatically supply water at a predetermined interval for each plant, and analyze the growth state of the plant and notify the same. 1. A wall landscaping system for easy growth management with automatic watering and plant growth analysis , which creates a vertical garden on an interior or exterior wall (W) of a structure , the system comprising:{'b': '10', 'a main body () installed to cover the wall (W) to have a plant grown thereon;'}{'b': 20', '10, 'a watering part () disposed at an upper end of the main body () to supply water to the plant;'}{'b': 30', '10, 'a detector () disposed around the main body () to photograph the plant; and'}{'b': 40', '30, 'a controller () automatically checking and recording a growth state of the plant by analyzing an image photographed by the detector (), and remotely notifying a manager according to the growth state of the plant.'}210111211131214131514. The system of claim 1 , wherein the main body () includes: multiple skeletal pipes () attached to the wall (W) in a form of a grid; a support plate () attached to outer surfaces of the skeletal pipes () and finished in a plane; a waterproof cloth () attached to an outer surface of the support plate () to block moisture; a root-resistant film () attached to an outer surface of the waterproof ...

NAVIGATION SYSTEM

A navigation-system for use on an automated vehicle includes a perception-sensor and a controller. The perception-sensor detects objects present proximate to a host-vehicle and detects a gradient of an area proximate to the host-vehicle. The controller is in communication with the perception-sensor. The controller is configured to control the host-vehicle. The controller determines a free-space defined as off of a roadway traveled by the host-vehicle, and drives the host-vehicle through the free-space when the gradient of the free-space is less than a slope-threshold and the objects can be traversed. 1. A navigation-system for use on an automated vehicle , said system comprising:a perception-sensor that detects objects present proximate to a host-vehicle and detects a gradient of an area proximate to the host-vehicle; anda controller in communication with the perception-sensor, said controller configured to control the host-vehicle, wherein the controller determines a free-space defined as off of a roadway traveled by the host-vehicle, and drives the host-vehicle through the free-space when the gradient of the free-space is less than a slope-threshold and the objects can be traversed.2. The system in accordance with claim 1 , wherein the controller distinguishes between the objects that are a barrier and the objects that are grass based on the perception-sensor.3. The system in accordance with claim 2 , wherein the controller further determines a height of the grass.4. The system in accordance with claim 1 , wherein the slope-threshold is determined based on a dynamic-model of the host-vehicle.5. The system in accordance with claim 1 , wherein the controller further determines a path to drive the host-vehicle from the roadway through the free-space and return to the roadway.6. The system in accordance with claim 1 , wherein the system further includes an alert-device in communication with the controller claim 1 , wherein the alert-device notifies an operator of the ...

METHOD FOR PROPERTY FEATURE SEGMENTATION

The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S 1. A method , comprising:receiving a region image depicting a property feature;determining an instance-aware mask for the property feature based on the region image;determining a semantic segmentation mask for the property feature based on the region image;computing a distance transform from the instance-aware mask; andgenerating a pixel-accurate mask by associating instance identifiers for different property feature instances with pixels of the semantic segmentation mask based on the distance transform.2. The method of claim 1 , wherein generating the pixel-accurate mask comprises determining a nearest property feature instance for pixels lacking an instance identifier within the semantic segmentation mask claim 1 , based on the distance transform.3. The method of claim 2 , wherein the nearest property feature instance is determined using a watershed transform.4. The method of claim 1 , wherein the instance-aware mask is further determined based on parcel data for a geographic region depicted in the region image.5. The method of claim 4 , wherein the parcel data comprises a parcel mask for the geographic region.6. The method of claim 1 , wherein the instance identifiers are further assigned to pixels of the semantic segmentation mask based on parcel data for a region depicted in the region image.7. The method of claim 1 , wherein the instance-aware mask comprises an under-segmented mask of the property feature.8. The method of claim 1 , wherein the instance-aware mask is determined by an instance-aware segmentation module trained on training data claim 1 , wherein the training data is determined by: ...

VEGETATION INDEX CALCULATION APPARATUS, VEGETATION INDEX CALCULATION METHOD, AND COMPUTER READABLE RECORDING MEDIUM

A vegetation index calculation apparatus () is provided with a specification unit () that collates height distribution data indicating a distribution of the height of plants that exist in a target region with predicted height data of a crop targeted for calculation of a vegetation index, and specifies a region where the crop exists within the target region, and a vegetation index calculation unit () that calculates the vegetation index of the crop that exists in the region specified by the specification unit (). 1. A vegetation index calculation apparatus comprising:a specification unit configured to collate height distribution data indicating a distribution of a height of plants that exist in a target region with predicted height data of a crop targeted for calculation of a vegetation index, and specify a region where the crop exists within the target region; anda vegetation index calculation unit configured to calculate the vegetation index of the crop that exists in the region specified by the specification unit.2. The vegetation index calculation apparatus according to claim 1 , further comprising:an image acquisition unit configured to acquire an aerial image of the target region;a stereo image generation unit configured to generate a stereo image from the aerial image acquired by the image acquisition unit; anda height distribution data generation unit configured to calculate the heights of plants that exist in the target region, by performing stereo matching processing on the stereo image generated by the stereo image generation unit, and generate the height distribution data using the calculated heights,wherein the vegetation index calculation unit calculates the vegetation index of the crop, using an image of the region specified by the specification unit within the aerial image.3. The vegetation index calculation apparatus according to claim 1 ,wherein the predicted height data of the crop is generated by performing growth simulation that uses a crop ...

SEMANTIC STRUCTURE FROM MOTION FOR ORCHARD RECONSTRUCTION

A method includes constructing a three-dimensional model of a front side of a row of trees based on a plurality of images of the front side of the row of trees and constructing a three-dimensional model of a back side of the row of trees based on a plurality of images of the back side of the row of trees. The three-dimensional model of the front side of the row of trees is merged with the three-dimensional model of the back side of the row of trees by linking a trunk in the three-dimensional model of the front side to a trunk in the three-dimensional model of the back side to form a merged three-dimensional model of the row of trees. The merged three-dimensional model of the row of trees is used to determine a physical attribute of the row of trees. 1. A method comprising:constructing a three-dimensional model of a front side of a row of trees based on a plurality of images of the front side of the row of trees;constructing a three-dimensional model of a back side of the row of trees based on a plurality of images of the back side of the row of trees;merging the three-dimensional model of the front side of the row of trees with the three-dimensional model of the back side of the row of trees by linking a trunk in the three-dimensional model of the front side to a trunk in the three-dimensional model of the back side to form a merged three-dimensional model of the row of trees; andusing the merged three-dimensional model of the row of trees to determine a physical attribute of the row of trees.2. The method of wherein merging the three-dimensional model of the front side of the row of trees with the three-dimensional model of the back side of the row of trees comprises:projecting the three-dimensional model of the front side of the row of trees onto a plane to form a first projection;projecting the three-dimensional model of the back side of the row of trees onto the plane to form a second projection;aligning the second projection with the first projection to ...

SOLID-STATE IMAGING ELEMENT, ELECTRONIC DEVICE, AND METHOD OF MANUFACTURING ELECTRONIC DEVICE

A ripple is suppressed in a solid-state imaging element that obtains a spectral spectrum. The solid-state imaging element includes a surface layer, a filter layer, and a photoelectric conversion layer. In the solid-state imaging element, the surface layer has a thickness exceeding a half of a coherence length of incident light. Furthermore, in the solid-state imaging element, the filter layer transmits predetermined target light of the incident light transmitted through the surface layer and reflects a rest of the incident light transmitted through the surface layer to the surface layer. Furthermore, in the solid-state imaging element, the photoelectric conversion layer photoelectrically converts the predetermined target light transmitted through the filter layer. 1. A solid-state imaging element comprising:a surface layer that has a thickness exceeding a half of a coherence length of incident light;a filter layer that transmits predetermined target light of the incident light transmitted through the surface layer and reflects a rest of the incident light transmitted through the surface layer to the surface layer; anda photoelectric conversion layer that photoelectrically converts the predetermined target light transmitted through the filter layer.2. The solid-state imaging element according to claim 1 , whereinthe surface layer includes an antireflection film.3. The solid-state imaging element according to claim 2 , whereinthe surface layer further includes a silicon dioxide layer.4. The solid-state imaging element according to claim 2 , whereinthe surface layer further includes a transparent resin layer.5. The solid-state imaging element according to claim 4 , whereinthe surface layer further includes a stress relaxation resin layer.6. The solid-state imaging element according to claim 1 , whereinthe filter layer includes a surface plasmon resonance filter.7. The solid-state imaging element according to claim 1 , whereinthe filter layer includes a Fabry-Perot ...

METHOD FOR RECOVERING A POSTERIORI INFORMATION ABOUT THE OPERATION OF A PLANT FOR AUTOMATIC CLASSIFICATION AND SORTING OF FRUIT

A method for recovering a posteriori information about the operation of a plant for automatic classification and sorting of fruit, wherein digital images of a reference piece of fruit previously processed by means of the plant are processed so as to identify its first group of individualizing features, which are then compared with each of a series of second groups of individualizing features each corresponding to a different piece of fruit processed by the plant, in order to establish a match; then, there is extraction from an electronic database of operating information which corresponds to information used or generated by the plant for automatic classification and sorting for classification of the relative processed piece of fruit and/or to information about sorting of the relative piece of fruit processed by the plant for automatic classification and sorting, where in the electronic database the operating information is associated with the second group of individualizing features for which during the comparison step the match with the first group of individualizing features has been established. 1. A method for recovering a posteriori information about the operation of a plant for automatic classification and sorting which automatically divides pieces of fruit into a plurality of different qualitative classes , the method comprising the following operating steps:an acquisition step wherein one or more digital images of a reference piece of fruit are acquired, where the reference piece of fruit is a piece of fruit previously processed by means of the plant for automatic classification and sorting;a processing step wherein the one or more digital images are processed by an electronic device, so as to identify a first group of individualizing features of the reference piece of fruit;a comparison step wherein an electronic device is used to compare the first group of individualizing features with each of a series of second groups of individualizing features each ...

CULTIVATION SYSTEM HAVING CULTIVATION BOX AND CONTROLLING METHOD THEREOF

Disclosed a plant cultivation system including: a replaceable cultivation box including a housing, and identification information of the cultivation box, and an electronic device configured to acquire the identification information from the cultivation box, detect state information about an inside of the cultivation box or around the cultivation box using a sensor, and spray liquid into the cultivation box based on the identification information and the state information. 1. An electronic device comprising:a support configured to support a cultivation box for cultivation of a plant;a nozzle configured to be inserted into the cultivation box based on the cultivation box being seated on the support;a liquid supplier configured to spray liquid into the cultivation box through the nozzle;a sensor configured to detect state information inside or around the cultivation box;a recognition circuitry configured to acquire identification information identifying the cultivation box from the cultivation box based on the cultivation box being seated on the support; anda processor connected to the liquid supplier, the sensor, and the recognition circuitry,wherein the processor is configured to:identify an operation state of the liquid supplier based on the state information and the identification information based on the cultivation box being seated on the support; andcontrol the liquid supplier to spray the liquid into the cultivation box based on the identified operation state.2. The electronic device of claim 1 , wherein the recognition circuitry includes a camera claim 1 ,wherein the processor is configured to:recognize an image code from an image acquired using the camera; andacquire the identification information using the image code.3. The electronic device of claim 1 , wherein the recognition circuitry includes an RFID (radio frequency identification) reader comprising circuitry configured to read an RFID claim 1 , wherein the identification information includes RFID ...

GENERATING REAL-TIME SENSOR MAPS FROM VIDEOS AND IN-GROUND SENSOR DATA

An apparatus for generating precision maps of an area is disclosed. The apparatus receives sensor data, where the sensor data includes sensor readings each indicating a level of a parameter in one of a plurality of first portions of an area, and video data representing an aerial view of the area. The sensor data may be received from sensors that are each deployed in one of the first portions of the area. The video data may be received from an aerial vehicle. An orthomosaic may be generated from the video data, and the orthomosaic and the sensor data used to generate a predication model. The prediction model may then be used to extrapolate the sensor data to determine a level of the parameter in each of a plurality of second portions of the area. A precision map of the area may be generated using the extrapolated sensor readings. 1. An apparatus , the apparatus comprising:one or more processors; and,memory in communication with the one or more processors, the memory comprising code that, when executed, causes the one or more processors to control the apparatus to:receive video data representing an aerial view of an area;receive sensor data, the sensor data generated from a plurality of sensor readings, each indicating a level of a parameter in one of a plurality of first portions of the area;generate an orthomosaic from the video data;generate a prediction model using the orthomosaic and the sensor data;extrapolate the sensor data to determine a level of the parameter in each of a plurality of second portions of the area using the prediction model; and,generate a map of the area, the map displayable to indicate the level of the parameter in each of the first and second portions of the area.2. The apparatus of claim 1 , wherein the parameter includes a heat measurement and the map includes a heat map of the area.3. The apparatus of claim 1 , wherein the parameter includes a moisture measurement and the map includes a moisture map of the area.4. The apparatus of claim ...

Systems and Methods for Virtual Agronomic Sensing

Systems and methods for virtual agronomic sensing are provided. In embodiments methods comprise receiving first agronomic data for a first geographic location comprising sensor data from agronomic sensors at the first geographic location; receiving first agronomic information for the first geographic location; and generating first predictive agronomic data for the first geographic location using the first agronomic data for the first geographic location and the first agronomic information for the first geographic location. Methods may further comprise testing the first predictive agronomic data for the first geographic location to be used for a second geographic location; receiving second agronomic information for the second geographic location; generating virtual agronomic sensors for the second geographic location as a function of the testing the first predictive agronomic data for the first geographic location; and providing second predictive agronomic data for the second geographic location using the virtual agronomic sensors. 1. A system for virtual agronomic sensing , comprising:one or more agronomic sensors at a first geographic location;at least one processor; anda memory storing processor-executable instructions, wherein the at least one processor is configured to implement the following operations upon executing the processor-executable instructions:receiving first agronomic data for the first geographic location, the first agronomic data for the first geographic location comprising sensor data from the one or more agronomic sensors at the first geographic location;receiving first agronomic information for the first geographic location;generating first predictive agronomic data for the first geographic location using the first agronomic data for the first geographic location and the first agronomic information for the first geographic location;testing the first predictive agronomic data for the first geographic location to be used for a second geographic ...

AGRICULTURAL WORK MACHINE

An agricultural work machine, in particular a harvester, has a header for performing agricultural work and having a control device which has at least one sensor unit for detecting a crop stream in and/or around the header and an image processing unit for processing images which are generated by the sensor unit based on the crop stream detected via sensor. The control device is configured to detect regions of like characteristics, components of the header and properties of the crop stream and is configured to use that which has been detected for open loop control and/or closed loop control of process sequences in the agricultural work machine. 1. An agricultural work machine , comprising:a header for performing agricultural work, anda control device which has at least one sensor unit configured for detecting a crop stream in and/or around the header and an image processing unit for processing images which are generated by the sensor unit based on the crop stream detected via the sensor,wherein the control device is configured to detect regions of like characteristics, components of the header and properties of the crop stream, and is configured to use that which has been detected for open loop control and/or closed loop control of process sequences in the agricultural work machine.2. The agricultural work machine according to claim 1 , wherein the image processing unit is configured to determine as a region (A) a field stand in front of the header and to identify it as a field stand in front of the header claim 1 , and/or to determine as a region (B) the field stand behind the header and to identify it as a field stand behind the header claim 1 , and/or to determine as a component (C) a crop pickup device (of the header and to identify it as a crop pickup device claim 1 , and/or to determine as a component (D) a crop cutting device of the header and to identify it as crop cutting device claim 1 , and/or to determine as a component (E) a crop conveying device of the ...

AGRICULTURAL WORK APPARATUS, AGRICULTURAL WORK MANAGEMENT SYSTEM, AND PROGRAM

An object of the present invention is to provide an agricultural work apparatus, an agricultural work management system, and a program capable of appropriately managing agricultural work information and smoothly implementing agricultural work and business in the future by making use of agricultural work information. An agricultural work apparatus may include: a first image-capturing device; an agricultural work determination unit that determines, on the basis of first image information obtained by capturing an image of a target of agricultural work through the first image-capturing device, whether or not to execute the agricultural work on the target; an agricultural work execution unit that executes the agricultural work; an agricultural work information generation unit that generates agricultural work information including a result of the agricultural work; a measurement unit that measures a position of the target; and an agricultural work information management unit that manages the agricultural work information and positional information. 1. An agricultural work apparatus , comprising:a first image-capturing device;an agricultural work determination unit that determines, on the basis of first image information obtained by capturing an image of a target of agricultural work through the first image-capturing device, whether or not to execute the agricultural work on the target;an agricultural work execution unit that executes the agricultural work on the basis of a determination result of the agricultural work determination unit;an agricultural work information generation unit that generates agricultural work information including a result of the agricultural work;a measurement unit that measures a position of the target; andan agricultural work information management unit that manages the agricultural work information and positional information indicating the measured position.2. The agricultural work apparatus according to claim 1 , further comprising:an ...

Autonomous laser weed eradication

Disclosed herein are methods, devices, modules, and systems which may be employed for automated weed identification, control, and eradication. These methods, devices, modules, and systems provide an alternative to hand cultivation or chemical herbicides. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.

SYSTEMS FOR ADJUSTING AGRONOMIC INPUTS USING REMOTE SENSING, AND RELATED APPARATUS AND METHODS

Methods, systems, and apparatus, including computer programs encoded on computer storage media for using remote sensing data to infer agronomic inputs to an agronomic simulation model. 1. A method comprising:identifying, based on data from an agronomic simulation model, a first indication of existence of a first agricultural characteristic in a particular portion of a first geographic region;receiving remote sensing data associated with the first geographic region, the received remote sensing data having been obtained using one or more remote sensing devices;identifying, based on the received remote sensing data, a second indication of existence of a second agricultural characteristic in the particular portion of the first geographic region;determining that the second indication is distinct from the first indication; andin response to determining that the second indication is distinct from the first indication, inferring one or more inputs to the agronomic simulation model based on the received remote sensing data to account for the existence of the second agricultural characteristic as indicated by the second indication.2. The method of claim 1 , further comprising:identifying, based on second data from an agronomic simulation model, a third indication of existence of a third agricultural characteristic in a particular portion of a second geographic region;receiving second remote sensing data associated with the second geographic region, the received second remote sensing data having been obtained using one or more remote sensing devices;identifying, based on the received second remote sensing data, a fourth indication of existence of a fourth agricultural characteristic in the particular portion of the second geographic region;determining that the fourth indication is substantially in accordance with the third indication; andbased on determining that the fourth indication is substantially in accordance with the third indication, confirming a validity of the third ...

SYSTEMS FOR DETERMINING AGRONOMIC OUTPUTS FOR A FARMABLE REGION, AND RELATED METHODS AND APPARATUS

A method may include obtaining data indicative of multiple agronomic scenarios, the data for each agronomic scenario including a hypothetical value for each of one or more agronomic inputs, in which the data for each of the agronomic scenarios is distinct from the data for each other one of the agronomic scenarios. The method may further include, for each of the multiple agronomic scenarios: predicting a predicted value for an agronomic output of a farmable region based on (i) the hypothetical value for each of the one or more agronomic inputs for the agronomic scenario and (ii) a measured value for each of at least one agronomic input of the farmable region. The method may further include generating a distribution of the predicted values for the agronomic output across the multiple agronomic scenarios. 1. A method comprising:obtaining data indicative of multiple agronomic scenarios, the data for each agronomic scenario including a hypothetical value for each of one or more agronomic inputs, in which the data for each of the agronomic scenarios is distinct from the data for each other one of the agronomic scenarios;for each of the multiple agronomic scenarios: predicting a predicted value for an agronomic output of a farmable region based on (i) the hypothetical value for each of the one or more agronomic inputs for the agronomic scenario and (ii) a measured value for each of at least one agronomic input of the farmable region; andgenerating a distribution of the predicted values for the agronomic output across the multiple agronomic scenarios.2. The method of claim 1 , comprising generating the hypothetical values for at least one of the multiple agronomic scenarios.3. The method of claim 1 , comprising determining a characterization of the farmable region based on the distribution of the predicted values for the agronomic output across the multiple agronomic scenarios.4. The method of claim 3 , in which determining the characterization comprises determining a ...

METHOD AND SYSTEM FOR THE MANAGEMENT OF AN AGRICULTURAL PLOT

The present invention relates to a decision support method for the management of an orchard of fruit trees, the method comprising the following steps: 112-. (canceled)13. A decision support method for the management of a fruit tree orchard , said decision support method comprising the following phases:Receiving a plurality of digital colour images from one of an optical sensor and a camera, wherein the digital images are acquired along the rows of the fruit tree orchard, and wherein each image represents a respective fruit element of the fruit tree orchard, Detecting objects of interest in each current image,', 'Counting the number of objects of interest detected in each current image,', 'Determining a blooming level for each current image, wherein the blooming level is determined as a function of the number of objects of interest counted for said current image,, 'Processing the plurality of acquired images for obtaining a plurality of processed images, said processing phase comprising, for each current image of the plurality of images, the following steps Merging the blooming levels of the fruit elements by areas along the rows of the fruit tree orchard,', 'Generating a map of the blooming levels of the fruit elements of the fruit tree orchard., 'Post-processing the plurality of processed images, said post-processing phase comprising the following steps141. The decision support method according to claim , wherein the merging step comprises assigning to each fruit element considered in a row of the fruit tree orchard the blooming level of the fruit elements adjacent to said fruit element considered in its row if at least one adjacent fruit element preceding the considered fruit element and at least one adjacent fruit element following the fruit element considered in its row have the same blooming level different from the blooming level of the considered fruit element.151. The decision support method according to claim , wherein the step of counting the number of ...

SYSTEMS AND METHODS FOR DIGITAL ELEVATION MAP FILTERS FOR THREE DIMENSIONAL POINT CLOUDS

Discussed herein are systems, devices, and methods for filtering digital elevation map (DEM) data. A method can include ingesting digital elevation map (DEM) data and intensity data from a panchromatic (PAN) or multi-spectral (MS) image, filling in voids in the ingested DEM data using local interpolation to create interpolated DEM data, creating a shadow map based on the received intensity data, modifying, using the created shadow map, a height of one or more pixels in the interpolated DEM data to create a modified DEM file, and storing the modified DEM file in a memory. 1. A non-transitory machine-readable storage device including instructions stored thereon that , when executed by one or more processors , cause the one or more processors to perform operations for filtering digital elevation map (DEM) data of a three dimensional point cloud , the operations comprising:ingest digital elevation map (DEM) data and intensity data from a panchromatic (PAN) or multi-spectral (MS) image;fill in voids in the DEM data using local interpolation to create interpolated DEM data;create a shadow map based on the received intensity data;modify, using the shadow map, a height of one or more pixels in the interpolated DEM data to create a modified DEM file; andstore, in a memory, the modified DEM file.2. The storage device of claim 1 , further comprising instructions stored thereon that claim 1 , when executed by the one or more processors claim 1 , cause the one or more processors to further create an edge map based on the received intensity data and wherein modification of the height of one or more pixels in the modified DEM file includes further modification of the height of one or more pixels in the modified DEM file based on the created edge map.3. The storage device of claim 2 , further comprising instructions stored thereon that claim 2 , when executed by the one or more processors claim 2 , cause the one or more processors to further create a foliage map and wherein ...

APPARATUS AND METHODS FOR PHENOTYPING PLANTS

An apparatus and methods for imaging and phenotyping of plants. Plants are placed in an enclosure having an access door and a floor. A turntable is disposed on the floor of the enclosure. A side view camera assembly attached to a frame of the apparatus is configured to capture images of a plant placed on the turntable. An overhead view camera assembly attached to the frame above the turntable is configured to capture images of a plant placed on the turntable. 1. An apparatus for capturing one or more images of one or more plants comprising:a frame;an enclosure having a floor;a turntable attached to the floor of the enclosurea side view camera assembly attached to the frame and adjacent to the turntable, the side view camera assembly configured to capture one or more images of a plant placed on the turntable;an overhead view camera assembly attached to the frame above the turntable, the overhead view camera assembly configured to capture one or more images of a plant placed on the turntable;a computer electronically connected to the turntable, the side view camera assembly, and the overhead view camera assembly; anda monitor connected to the computer, the monitor configured to display information related to a plant placed on the turntable.2. The apparatus of wherein the side view camera assembly comprises a vibration sensor configured to detect vibration of a movable camera.3. The apparatus of wherein the overhead view camera assembly comprises a vibration sensor configured to detect vibration of a movable camera.4. The apparatus of further comprising a corner view camera assembly attached to the frame claim 1 , the corner view camera assembly configured to capture one or more images of a plant placed on the turntable claim 1 , and wherein the computer is electronically connected to the corner view camera assembly.5. A method for capturing one or more images of one or more plants comprising:providing a plant to be imaged;placing the plant on the platform of a plant ...

ENHANCING TEMPORAL AND SPATIAL RESOLUTION AND CORRECTING DATA ANOMALIES OF REMOTE SENSED DATA FOR ESTIMATING HIGH SPATIO-TEMPORAL RESOLUTION VEGETATION INDICES

A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data. 1. A virtual satellite system , comprising:a hardware processor coupled with a communication interfacea memory coupled to the hardware processor;a blending algorithm executable on the hardware processor;an application programming interface executing on the hardware processor, the application programming interface responsive to being invoked triggering the blending algorithm executing on the hardware processor to:receive data corresponding to a historical time window and representing a time series of images of a geographic region from a plurality of satellites, at least some of which acquire images at different spatial resolutions from one another;identify a first satellite that has a minimum (finest but minimum in value) of the spatial resolutions of the plurality of satellites;re-project the data of the plurality of satellites except the first satellite to the minimum spatial resolution identified in the first satellite;remove cloud data, if present, from the data of the first satellite and from the re-projected data of remaining satellites of the plurality of the satellites;interpolate the data of the first satellite and the re-projected data of the remaining satellites to a desired temporal resolution, for the data not already having the desired temporal resolution;determine pairwise bias between the data, possibly interpolated and cloud corrected, of the first satellite and the re-projected data, ...

DETECTION AND REPLACEMENT OF TRANSIENT OBSTRUCTIONS FROM HIGH ELEVATION DIGITAL IMAGES

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image. 1. A method implemented using one or more processors , comprising:obtaining a digital image of a geographic area captured from an elevated vantage point, wherein one or more obscured portions of the geographic area are obscured by one or more transient obstructions depicted in the digital image; andgenerating a synthetic digital image based on the digital image, wherein in the synthetic digital image, pixels of one or more of the transient obstructions are replaced with predicted pixels representing underlying terrain, wherein the generating includes applying the digital image of the geographic area as input across a generator model, wherein the generator model is trained as part of a generative adversarial network that also included a discriminator model that is trained in conjunction with the generator model.2. The method of claim 1 , wherein the digital image is captured by a satellite claim 1 , and the elevated vantage point lies on a ...

Vine Growing Management Method and Apparatus With Autonomous Vehicles

In some embodiments, a method for managing growing vine in a vineyard includes operating one or more unmanned aerial vehicles (UAV) to fly over a plurality of sections of a vineyard. The UAVs are fitted with a plurality of cameras equipped to generate images in a plurality of spectrums. The plurality of sections of the vineyard grow vines of a plurality of varietals. The method further includes taking a plurality of aerial images of the sections of the vineyard in the plurality of spectrums, using the plurality of cameras, while the UAVs are flying over the plurality of sections of the vineyard, and executing an analyzer on a computing system to machine analyze the plurality of aerial images for anomalies associated with growing the vines of the plurality of varietals. The machine analysis takes into consideration topological information of the vineyard, as well as current planting information of the vineyard.

PLANT PROVENANCE AND DATA PRODUCTS FROM COMPUTER OBJECT RECOGNITION DRIVEN TRACKING

This disclosure sets forth systems and techniques that trace plant provenance and determine a likelihood of plant spoilage within a plant growth operation. Particularly, a plant health monitoring system may monitor and record plant growth data of one or more individual plants during a plant life cycle from germination through to harvest. The plant growth data may include data relating to environmental conditions that surround the individual plants, and plant-specific data drawn from time-lapse images captured during the plant life cycle. A plant grower may use the time-lapse image data to identify changes in plant structure that indicate the degree of health of an individual plant, or the encompassing batch of plants. Further, a plant grower may use the plant growth data to generate one or more financial instruments that may be distributed to one or more financial institutions as support for securing capital to fund the plant growth operation. 1. A system , comprising:an image capturing device;one or more processors;memory coupled to the one or more processors, the memory including one or more modules that are executable by the one or more processors to:receive, from the image capturing device, a first image of a first plant within a batch of plants;identify a dataset of stored images of a second plant, the dataset of stored images including stored images of a plurality of health conditions of the second plant, the second plant being a same plant type as the first plant;determine a similarity between the first image and a second image of the dataset of stored images; andgenerate a health metric for the first plant that quantifies a risk of plant spoilage prior to harvest, the health metric being based at least in part on the similarity.2. The system as recited in claim 1 , wherein the similarity is a first similarity and the dataset of stored images is a first data set of stored images claim 1 , and wherein the one or more modules are further executable by the one ...

HI-FIDELITY COMPUTER OBJECT RECOGNITION BASED HORTICULTURAL FEEDBACK LOOP

Infrastructure and methods to implement a feedback loop for a horticultural operation are disclosed. Image capture devices are deployed on a per plant or near per plant basis, and upload images to image analysis server. The image analysis server applies image preprocessing to the uploaded images, and then applies image analysis to identify plants and plant artifacts. Where artifacts indicate an issue with a plant, a course of action to remediate is identified. Static analysis techniques, sequential analysis techniques, and adaptive techniques are some of the techniques enabled and disclosed by the application of object recognition techniques to horticultural applications. Images may be associated with a plant state vector to enable complex comparison operations. 1. A system to detect anomalies on a plant , comprising:a processor;a memory communicatively coupled to the processor;an image retriever software component, resident in the memory, configured to receive a plurality of images of a target;an identifier software component, resident in the memory, configured to identify a plant as the target in an image and at least one artifact of the plant; andan analyzer software component, resident in the memory, configured to identify issues in a plant as the target in an image, based on at least one artifact of the plant.2. The system of claim 1 , comprising:an image preprocessor software component, resident in the memory, configured to perform at least one of the following:a histogram generation of red-green-blue (RGB) measurements in an image;a histogram generation of hue-luminance-saturation (HLS) measurements in an image;an iteration count of morphological opens and/or closes of an image;a Gaussian blur transformation in an image; andan application of Otsu segmentation to an image.3. The system of claim 2 , wherein the image preprocessor software component is further configured to determine whether an image has sufficient content for further processing.4. The system of ...

APPARATUS, SYSTEM AND METHODS FOR IMPROVED VERTICAL FARMING

The present disclosure is directed to improved vertical farming using autonomous systems and methods for growing edible plants, using improved stacking and shelving units configured to allow for gravity-based irrigation, gravity-based loading and unloading, along with a system for autonomous rotation, incorporating novel plant-growing pallets, while being photographed and recorded by camera systems incorporating three dimensional/multispectral cameras, with the images and data recorded automatically sent to a database for processing and for gauging plant health, pest and/or disease issues, and plant life cycle. The present disclosure is also directed to novel harvesting methods, novel modular lighting, novel light intensity management systems, real time vision analysis that allows for the dynamic adjustment and optimization of the plant growing environment, and a novel rack structure system that allows for simplified building and enlarging of vertical farming rack systems. 1. A vertical farming system for optimizing a plant growing process , comprising:a shelving system, said shelving system comprising a plurality of shelves, each of said plurality of shelves having a first end and a second end with said first end being located higher than said second end, each of said plurality of shelves configured to accept and secure at least one grow tray, said plurality of shelves comprising a plurality of rollers, said plurality of rollers capable of rotating and configured such that a grow tray placed on said rollers will be transported from said first end to said second end using gravitational force;a lift, said lift positioned at the said second end of said plurality of shelves, said lift configured to remove said grow tray from a first of said plurality of shelves and moving said grow tray to a second of said plurality of shelves;said grow tray comprising at least one port, said port located and configured on said grow tray such that when said grow tray fills with water ...