Abstract: Systems and methods are provided for identifying rogue plants in an agricultural field. One example computer-implemented system includes accessing data specific to an agricultural field, where the data includes an image of the field captured, by at least one capture device, during a particular growth stage of a crop in the field. The method also includes applying a trained classifier model to the sub-images from the image and classifying, using the trained classifier model, each of the multiple plants included in the sub-images as a rogue plant or as not a rogue plant.

Abstract: Techniques are described for generating population counts for seed production plants based on one or more images obtained from a camera on an aerial vehicle including, but not limited to, UAVs. The image(s) is processed to identify the plurality of rows of seed production plants, classify each one of the identified rows as either male or female, and produce a count of the number of seed production plants in the male rows and produce a count of the number of seed production plants in the female rows. The seed production plants can be corn seed plants or any other type of seed production plant in which male and female seed production plants can be distinguished from one another based on one or more aerial images.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: An agricultural zone management system and methods where a variable rate prescription (VRP) includes a plurality of equipment zones of the agricultural field that are generated based on at least one treatment dimension of a farm implement to be used in the agricultural field, and each one of the equipment zones is further defined based on one or more of an intended direction of travel of the farm implement in the agricultural field, and an intended travel path of the farm implement in the agricultural field. In addition, a treatment plan, such as a treatment rate, can be generated based on the needs of plants, soil or the like in each equipment zone.

Abstract: An agricultural zone management system and methods where agricultural management zones can be generated based on point samples generated from images of a field. The images can be captured by a camera on a UAV flown over the field. The point samples are used to generate the management zones of the agricultural field, where each management zone encompasses at least one of the point samples. The management zones are then stored in at least one non-transitory computer readable storage medium. The management zones can then be used to, for example, treat the field using a prescription that has been developed for each management zone, make yield predictions of a crop grown or to be grown in the field, perform analysis of crops or soil in the field, make plans for the future use of the field, and others.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: Autonomous vehicles may include one or more onboard devices to perform various actions, such as a still image capture device. In contrast with using an auxiliary communication system to control a payload, a vehicle navigation sensor is used to monitor autonomous vehicle movements to match a predefined vehicle maneuver event, and trigger a payload event based on identification of the vehicle maneuver event. For example, this allows an autopilot system or a remote drone pilot to initiate an image capture device or send other commands based on vehicle maneuver event recognition.

Abstract: 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.

Abstract: Autonomous vehicles may include one or more onboard devices to perform various actions, such as a still image capture device. In contrast with using an auxiliary communication system to control a payload, a vehicle navigation sensor is used to monitor autonomous vehicle movements to match a predefined vehicle maneuver event, and trigger a payload event based on identification of the vehicle maneuver event. For example, this allows an autopilot system or a remote drone pilot to initiate an image capture device or send other commands based on vehicle maneuver event recognition.

Abstract: Index-based geospatial analysis may include applying a first and second index-based analysis for a set of imagery. The set of imagery may include a location-specific index values used to form a histogram for a single index image (e.g., for a single surveyed field). This first analysis may be referred to as an “acre-to-acre” mapping, which may be useful for identifying differences in indices (e.g., NDVI vegetative health) of different parts of the field from a single day. A second “day-to-day” index-based analysis may be performed by calculating a histogram for each set of imagery from multiple days, combining the histograms, and generating a single equal-area index map. The index map can be applied to redistribute the histogram values within multiple days of data, which may provide a more useful map of variation in each individual image and changes between images.

Abstract: 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.

Abstract: Autonomous vehicles may include one or more onboard devices to perform various actions, such as a still image capture device. In contrast with using an auxiliary communication system to control a payload, a vehicle navigation sensor is used to monitor autonomous vehicle movements to match a predefined vehicle maneuver event, and trigger a payload event based on identification of the vehicle maneuver event. For example, this allows an autopilot system or a remote drone pilot to initiate an image capture device or send other commands based on vehicle maneuver event recognition.

Abstract: 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.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: 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.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: Autonomous vehicles may include one or more onboard devices to perform various actions, such as a still image capture device. In contrast with using an auxiliary communication system to control a payload, a vehicle navigation sensor is used to monitor autonomous vehicle movements to match a predefined vehicle maneuver event, and trigger a payload event based on identification of the vehicle maneuver event. For example, this allows an autopilot system or a remote drone pilot to initiate an image capture device or send other commands based on vehicle maneuver event recognition.

Abstract: Index-based geospatial analysis may include applying a first and second index-based analysis for a set of imagery. The set of imagery may include a location-specific index values used to form a histogram for a single index image (e.g., for a single surveyed field). This first analysis may be referred to as an “acre-to-acre” mapping, which may be useful for identifying differences in indices (e.g., NDVI vegetative health) of different parts of the field from a single day. A second “day-to-day” index-based analysis may be performed by calculating a histogram for each set of imagery from multiple days, combining the histograms, and generating a single equal-area index map. The index map can be applied to redistribute the histogram values within multiple days of data, which may provide a more useful map of variation in each individual image and changes between images.