Abstract: In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting presence of a crop at particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining crop boundary locations within the particular portion of the geographical region based on the predicted presence of the crop at the particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indication of crop areas, wherein the crop areas are defined by the determined crop boundary locations.

Abstract: Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

Abstract: Implementations are described herein for analyzing vision data depicting undesirable plants such as weeds to detect various attribute(s). The detected attribute(s) of a particular undesirable plant may then be used to select, from a plurality of available candidate remediation techniques, the most suitable remediation technique to eradicate or otherwise eliminate the undesirable plants.

Abstract: Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s).

Abstract: Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

Abstract: Implementations are described herein for coordinating semi-autonomous robots to perform agricultural tasks on a plurality of plants with minimal human intervention. In various implementations, a plurality of robots may be deployed to perform a respective plurality of agricultural tasks. Each agricultural task may be associated with a respective plant of a plurality of plants, and each plant may have been previously designated as a target for one of the agricultural tasks. It may be determined that a given robot has reached an individual plant associated with the respective agricultural task that was assigned to the given robot. Based at least in part on that determination, a manual control interface may be provided at output component(s) of a computing device in network communication with the given robot. The manual control interface may be operable to manually control the given robot to perform the respective agricultural task.

Abstract: In embodiments, acquiring sensor data associated with a plant growing in a field, and analyzing the sensor data to extract one or more phenotypic traits associated with the plant from the sensor data. Indexing the one or more phenotypic traits to one or both of an identifier of the plant or a virtual representation of a part of the plant, and determining one or more plant insights based on the one or more phenotypic traits, wherein the one or more plant insights includes information about one or more of a health, a yield, a planting, a growth, a harvest, a management, a performance, and a state of the plant. One or more of the health, yield, planting, growth, harvest, management, performance, and the state of the plant are included in a plant insights report that is generated.

Abstract: Implementations are described herein for three-dimensional (“3D”) modeling of objects that target specific features of interest of the objects, and ignore other features of less interest. In various implementations, a plurality of two-dimensional (“2D”) images may be received from a 2D vision sensor. The plurality of 2D images may capture an object having multiple classes of features. Data corresponding to a first set of the multiple classes of features may be filtered from the plurality of 2D images to generate a plurality of filtered 2D images in which a second set of features of the multiple classes of features is captured. 2D-3D processing, such as structure from motion (“SFM”) processing, may be performed on the 2D filtered images to generate a 3D representation of the object that includes the second set of one or more features.

Abstract: Implementations set forth herein relate to using fiducial markings on one or more localized portions of an agricultural apparatus in order to generate local and regional data that can be correlated for planning and executing agricultural maintenance. An array of fiducial markings can be disposed onto plastic mulch that surrounds individual crops, in order that each fiducial marking of the array can operate as a signature for each individual crop. Crop data, such as health and yield, corresponding to a particular crop can then be stored in association with a corresponding fiducial marking, thereby allowing the certain data for the particular crop to be tracked and analyzed. Furthermore, autonomous agricultural devices can rely on the crop data, over other sources of data, such as GPS satellites, thereby allowing the autonomous agricultural devices to be more reliable.

Abstract: In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations.

Abstract: Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Abstract: In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting presence of a crop at particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining crop boundary locations within the particular portion of the geographical region based on the predicted presence of the crop at the particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indication of crop areas, wherein the crop areas are defined by the determined crop boundary locations.

Abstract: Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s).

Abstract: Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.

Abstract: In embodiments, acquiring sensor data associated with a plant growing in a field, and analyzing the sensor data to extract, while in the field, one or more phenotypic traits associated with the plant from the sensor data. Indexing, while in the field, the one or more phenotypic traits to one or both of an identifier of the plant or a virtual representation of a part of the plant, and determining one or more plant insights based on the one or more phenotypic traits, wherein the one or more plant insights includes information about one or more of a health, a yield, a planting, a growth, a harvest, a management, a performance, and a state of the plant. One or more of the health, yield, planting, growth, harvest, management, performance, and the state of the plant are included in a plant insights report that is generated.

Abstract: Implementations are described herein for three-dimensional (“3D”) modeling of objects that target specific features of interest of the objects, and ignore other features of less interest. In various implementations, a plurality of two-dimensional (“2D”) images may be received from a 2D vision sensor. The plurality of 2D images may capture an object having multiple classes of features. Data corresponding to a first set of the multiple classes of features may be filtered from the plurality of 2D images to generate a plurality of filtered 2D images in which a second set of features of the multiple classes of features is captured. 2D-3D processing, such as structure from motion (“SFM”) processing, may be performed on the 2D filtered images to generate a 3D representation of the object that includes the second set of one or more features.

Abstract: Systems and Methods for Augmented-Human Field Inspection Tools for Automated Phenotyping Systems and Agronomy Tools. In one embodiment, a method for plant phenotyping, includes: acquiring a first set of observations about plants in a field by a trainer. The trainer carries a sensor configured to collect observations about the plant, and the first set of observations includes ground truth data. The method also includes processing the first set of observations about the plants by a trait extraction model to generate instructions for a trainee; and acquiring a second set of observations about the plants by a trainee while the trainee follows the instructions.

Abstract: Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

Abstract: Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Abstract: A technique and apparatus for monitoring a plant canopy over a field is disclosed. The technique includes receiving first sensor values from a plurality of plant canopy sensors disposed in or on a ground of the field under the plant canopy. The first sensor values are indicative of near-infrared (IR) light reflected or reradiated from the plant canopy. Second sensor values are also received from the plant canopy sensors. The second sensor values are indicative of red light that is incident through the plant canopy. A map of the plant canopy may be generated based upon the first and second sensor values.