Abstract: A method for continuous real time monitoring of a crop growth including the use of a telescoping sensor mount capable of systematically extending a sensor above a crop growth canopy. The method includes determining a value associated with soil saturation, soil nitrogen mineralization, and growing degree units. These values are made available to the user real time. The telescoping sensor mount of the invention includes a foldaway tripod support system and is further capable of being powered with solar energy.

Abstract: A method for continuous real time monitoring of a crop growth including the use of a telescoping sensor mount capable of systematically extending a sensor above a crop growth canopy. The method includes determining a value associated with soil saturation, soil nitrogen mineralization, and growing degree units. These values are made available to the user real time. The telescoping sensor mount of the invention includes a foldaway tripod support system and is further capable of being powered with solar energy.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: A method of determining whether to treat soybeans for soybean aphids, the method includes collecting at least one image of a soybean canopy using one or more remote sensing instruments and processing the image into spectral reflectance data and selecting from the spectral reflectance data optimal spectral wavelength bands. The selected reflectance data is classified into one of a plurality of classification groupings using a machine learned classification model. To treat or not treat the soybean canopy for aphids is determined based on the classification of the reflectance data into one of the class groupings.

Abstract: A method of determining whether to treat soybeans for soybean aphids, the method includes collecting at least one image of a soybean canopy using one or more remote sensing instruments and processing the image into spectral reflectance data and selecting from the spectral reflectance data optimal spectral wavelength bands. The selected reflectance data is classified into one of a plurality of classification groupings using a machine learned classification model. To treat or not treat the soybean canopy for aphids is determined based on the classification of the reflectance data into one of the class groupings.

Abstract: Pixel color values representing an image of a portion of a field are received where each pixel color value has a respective position within the image. A processor identifies groups of the received pixel color values as possibly representing a Nitrogen-deficient plant leaf. For each group of pixel color values, the processor converts the pixel color values into feature values that describe a shape and the processor uses the feature values describing the shape to determine whether the group of pixel color values represents a Nitrogen-deficient leaf of a plant. The processor stores in memory an indication that the portion of the field is deficient in Nitrogen based on the groups of pixel color values determined to represent a respective Nitrogen-deficient leaf.

Abstract: A method of determining the nitrogen status of an area of land includes determining a critical nitrogen concentration for aboveground vegetation of plants in the area of land based on a dry weight biomass of entire plants and determining an actual nitrogen concentration for the aboveground vegetation of the plants. A critical nitrogen concentration for the entire plants is determined based on the dry weight biomass of the entire plants. The actual nitrogen concentration for the aboveground vegetation, the critical nitrogen concentration for the aboveground vegetation, the critical nitrogen concentration for the entire plants and the dry weight biomass of the entire plants are combined to form the nitrogen status for the area of land.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Pixel color values representing an image of a portion of a field are received where each pixel color value has a respective position within the image. A processor identifies groups of the received pixel color values as possibly representing a Nitrogen-deficient plant leaf. For each group of pixel color values, the processor converts the pixel color values into feature values that describe a shape and the processor uses the feature values describing the shape to determine whether the group of pixel color values represents a Nitrogen-deficient leaf of a plant. The processor stores in memory an indication that the portion of the field is deficient in Nitrogen based on the groups of pixel color values determined to represent a respective Nitrogen-deficient leaf.

Abstract: A system includes an unmanned aerial vehicle and an unmanned surface vehicle. The unmanned aerial vehicle has a memory storing a plurality of collection points and at least one sensor for collecting sensor data from each of the collection points. The unmanned surface vehicle is capable of moving to a plurality of locations. The unmanned aerial vehicle travels through the air between at least two collection points stored in the memory and the unmanned aerial vehicle is carried between at least two collection points stored in the memory by the unmanned surface vehicle.

Abstract: A system includes an unmanned aerial vehicle and an unmanned surface vehicle. The unmanned aerial vehicle has a memory storing a plurality of collection points and at least one sensor for collecting sensor data from each of the collection points. The unmanned surface vehicle is capable of moving to a plurality of locations. The unmanned aerial vehicle travels through the air between at least two collection points stored in the memory and the unmanned aerial vehicle is carried between at least two collection points stored in the memory by the unmanned surface vehicle.