Sentera, Inc.

Eric Taipale

Andrew Muehlfeld

Ryan Lee Nelson

Nicholas Witthoeft

Henry Nelson

Precision agriculture methods and systems where drone images of an agricultural field are captured by a UAV and analyzed to generate information regarding the agricultural field. Items of interest are identified in the images, the pixel-space locations of the items of interest are determined, and the world-space locations of the items of interest are then determined using the pixel-space locations. The transformation from pixel-space location to world-space location occurs without transforming the images or processing transformed images.

Precision agriculture using pose georeferenced analytics

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.

Multiple georeferenced aerial image crop analysis and synthesis

A method may include determining a location of an unmanned aerial vehicle (UAV); generating a set of mission plans based on the location of the UAV, each mission plan including respective mission plan parameters; receiving a selection of a mission plan of the set of mission plans; obtaining mission plan parameters for the selected mission plan; and transmitting the obtained mission plan parameters to the UAV to configure the UAV according to the obtained mission parameters.

Automatic location-based UAV mission plan loading

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.

Ground control point extraction from planting data

Spectral filtering or shaping is applied to an imaging system of an aerial vehicle such as a UAV to ensure that the measurements between a camera system and an ambient light sensor system are proportional under all light conditions. The spectral filtering is applied so that the resulting spectral sensitivity curves of the ambient light sensor system and of the camera system are proportional to one another over a spectral region. Thereafter, it is determined if there is a difference between a target ambient lighting condition and a real-time ambient lighting condition collected by the ambient light sensor system when an image was obtained. If a difference exists, a color correction can be applied to the image. This results in more accurate color correction and more spectrally accurate and consistent images.

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Minneapolis, MN, United States of America

Eric Taipale