Abstract: Systems and methods are described where a ground control station in communication with a UAV can render a low latency (but possibly lossy), essentially real-time video captured by the UAV, or render a substantially lossless, reconstructed version of the video stream, depending upon a video latency period selected by a user. The user is able to select the desired video latency period, for example via the ground control station, and can change the video latency period as desired including in real-time as the UAV is in flight and capturing video.

Abstract: One or more images are used to analyze permanent cropland. The images can be obtained from one or more sensors on an aerial vehicle including, but not limited to, UAVs. The analysis of the permanent cropland includes, but is not limited to, analyzing the plants growing in the permanent cropland or analyzing the permanent crops growing on the plants. In one embodiment, the analysis can include estimating a total yield of the permanent crops where the estimated permanent crop yield is more accurate than the estimated yield obtained using traditional methods.

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: A pitot probe assembly that is formed from modular, replaceable components, and is flexible. The configuration of the pitot probe assembly allows the pitot probe assembly to absorb and/or dissipate impact energy, and the modular, replaceable components allow for quick and easy repair of the pitot probe assembly. The pitot probe assembly can be configured as a total pressure pitot probe assembly or as a pitot static probe assembly.

Abstract: An aerial vehicle system, such as an unmanned aerial vehicle, that includes a camera system with replaceable camera modules that have different spectral, optical and/or sensing characteristics from one another. By replacing one camera module with another camera module, different information can be gathered and analyzed by the camera system of the aerial vehicle system.

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: 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.

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: 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: Systems and methods for providing an accurate determination of above ground level (AGL) altitude for use in precision agriculture. A 3D computer model of an agricultural field is generated by analyzing frames of a video of the agricultural field that have been obtained by a camera on an unmanned aerial vehicle flown over the agricultural field and generating 3D points that are used to create the 3D computer model. The 3D computer model is then scaled by fusing each 3D point with navigation sensor data associated with each frame. An AGL altitude for each 3D point of the scaled 3D computer model can then be determined. In another embodiment, a trajectory of the unmanned aerial vehicle can be determined and used to generate a georeferenced map using images captured by the camera.

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: An aerial imaging system is disclosed. The aerial imaging system includes an aerial vehicle; a camera system mounted on the aerial vehicle; and an image storage medium. The camera system includes a lens and a plurality of image sensors. The plurality of image sensors includes a reference image sensor and a plurality of fixed wavelength sensors. The reference image sensor is configured to capture images including light at a plurality of wavelength ranges and to output a multilayer image. The plurality of fixed wavelength sensors are each configured to capture images including light at a selected wavelength range and to each output a single layer image. The image storage medium is configured to store the multilayer image and the plurality of single layer images.

Abstract: 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.

Abstract: The present subject matter provides a technical solution for various technical problems associated with precision agriculture imagery. A technical solution for stitching together successive passes of captured images may include using multiple image capture devices, where the image capture devices have different fields of view. A first camera may be used to provide a first field of view (FOV), and may be configured to provide a selected ground sample distance (GSD) for precision agriculture imagery analysis. A second camera may be used to provide a wider FOV, and may be used to provide sufficient stitching process overlap between images captured in successive passes.

Abstract: An image capture device may capture visible and infrared light, and image analysis may be used to generate a map of the Normalized Difference Vegetation Index (NDVI) of healthy vegetation. Because NDVI data focuses on red and near infrared (NIR) reflectance of plants, NDVI data may be generated using an aircraft-mounted camera with optical filtering to collect various wavelengths. To reduce the size, weight, complexity, and cost of the image analysis system, a multiband optical filter may be used to capture multiple passbands simultaneously.

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: 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.