Hydroponics plant collars including a body defining a thickness, a center, and a periphery, a slot extending from the periphery and generally toward the center, a terminus of the slot and a surface thereof, and two opposing slot surfaces which define smooth plant contact portions thereof and which are spaced apart from one another whereby the body distributes forces acting on the hydroponics plant collar and on a plant to be in the slot, and metallic nanospheres dispersed throughout the body.

In some embodiments, the system is programmed to build from multiple training sets multiple digital models, each for recognizing plant diseases having symptoms of similar sizes. Each digital model can be implemented with a deep learning architecture that classifies an image into one of several classes. For each training set, the system is thus programmed to collect images showing symptoms of one or more plant diseases having similar sizes. These images are then assigned to multiple disease classes. For a first one of the training sets used to build the first digital model, the system is programmed to also include images that correspond to a healthy condition and images of symptoms having other sizes. These images are then assigned to a no-disease class and a catch-all class. Given a new image from a user device, the system is programmed to then first apply the first digital model. For the portions of the new image that are classified into the catch-all class, the system is programmed to then apply another one of the digital models. The system is programmed to finally transmit classification data to the user device indicating how each portion of the new image is classified into a class corresponding to a plant disease or no plant disease.

Plant scanning vehicles (10) including, but not limited to, plant scanning vehicles for use in field-based phenotyping. There is a central body (16); three or more legs (15) extending from the central body (16) to support a wheel (13) on each leg (15); wherein the three or more legs (15) are mounted to the central body (16) rotatably about a respective vertical axis (95) to allow adjustment of a track width W of the vehicle by rotating the legs wherein the legs are mechanically coupled to transmit rotation between the legs about their respective vertical axes and the central body (16) or the three or more legs (13) are configured to support a sensor (47) to scan plants.

The present technique relates to an image processing apparatus, an image processing method, and a program that can easily execute a stitching process.

Provided are: an image generation unit that generates a first reference image regarding a first imaging region on the basis of a plurality of first images regarding the first imaging region and that generates a second reference image regarding a second imaging region at least partially overlapping with the first imaging region on the basis of a plurality of second images regarding the second imaging region; and a processing unit that generates positioning information indicating a correspondence between the first imaging region and the second imaging region on the basis of the first reference image and the second reference image. The present technique can be applied to, for example, an image processing apparatus that executes a stitching process of a plurality of images.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

A method of determining a plant's biomass includes three steps, namely establishing an X-ray photograph of the plant, establishing an absorption characteristic of the plant in an X-ray photograph, and determining the plant's biomass by means of the absorption characteristic of the plant. Said determining is based on a predetermined relation between a reference absorption characteristic and a reference biomass.

A method of determining a plant's biomass includes three steps, namely establishing an X-ray photograph of the plant, establishing an absorption characteristic of the plant in an X-ray photograph, and determining the plant's biomass by means of the absorption characteristic of the plant. Said determining is based on a predetermined relation between a reference absorption characteristic and a reference biomass.