Yottamark, Inc.

X Development LLC

Mineral Earth Sciences LLC

Deere + Company

Trimble Navigation Limited

Trimble Inc.

Deere & Company

Elliott Grant

Zhiqiang Yuan

Cheng-en Guo

Jie Yang

J Scott Carr

Matthew Self

Benoit M Schillings

Sergey Yaroshenko

Gabriella Levine

Matthew A Bitterman

William Raymond Regan

Hongxu Ma

David R Brown

Albert Anthony Luna

Yueqi Li

Sean Calhoon

Warwick Robert Stirling

Wilson Zhao

Ming Zheng

Nanzhu Wang

David Clifford

Aleksandra Deis

Cheng-En Guo

Bodi Yuan

Daniel Ribeiro Silva

Francis Ebong

Hongxiao Liu

Linda Kanu

Juan Garibay

Robert Herdemann

Kevin Ma

Jeff Roughgarden

Lin Zhang

Matt VanCleave

Implementations are disclosed for automatic commissioning, configuring, calibrating, and/or coordinating sensor-equipped modular edge computing devices that are mountable on agricultural vehicles. In various implementations, neighbor modular edge computing device(s) that are mounted on a vehicle nearest a given modular edge computing device may be detected based on sensor signal(s) generated by contactless sensor(s) of the given modular edge computing device. Based on the detected neighbor modular edge computing device(s), an ordinal position of the given modular edge computing device may be determined relative to a plurality of modular edge computing devices mounted on the agricultural vehicle. Based on the sensor signal(s), distance(s) to the neighbor modular edge computing device(s) may be determined. Extrinsic parameters of the given modular edge computing device may be determined based on the ordinal position of the given modular edge computing device and the distance(s).

Automatically determining extrinsic parameters of modular edge computing devices

Techniques are described herein for determining whether a particular user is authorized to access data, corresponding to a particular field, in a farmland database. A method includes: receiving a location history corresponding to a user, the location history including historical location data that indicate, for each time of a plurality of times, a location of the user at the time; applying the historical location data as inputs across a trained machine learning model to predict a persona corresponding to the user; receiving, from the user, a request to access data; in response to receiving the request to access the data, determining that the request is authorized based on the predicted persona corresponding to the user; and in response to determining that the request is authorized, providing access to the data.

Persona prediction for access to resources

Implementations set forth herein relate to utilizing S2 cell values to characterize arbitrary portions of land parcels and storing the S2 cell values in association with a non-fungible token (NFT) that is stored on a blockchain network, or other peer-to-peer (P2P) network. The S2 cell values can be generated by iteratively using bounding shapes that are selected to extend over at least a portion of a respective parcel of land, and each bounding shape can be represented by one or more single dimensional values. When a generated bounding shape extends outside of a boundary of a parcel of land, subcells of the bounding shape can be generated to define further bounding shapes. A land NFT for the list of cell values for the bounding shapes can be stored at a blockchain address for an authenticated owner of the parcel of land.

Associating land NFTs with digital representations of land parcels

Implementations are described herein for colorizing an X-ray image and predicting one or more phenotypic traits about a plant based on the colorized X-ray image. In various implementations, an X-ray image that depicts a plant with a canopy of the plant partially occluding a part-of-interest is obtained, where the part-of-interest is visible through the canopy in the X-ray image. The X-ray images is colorized to predict one or more phenotypic traits of the part-of-interest. The colorization includes processing the X-ray image based on a machine learning model to generate a colorized version of the X-ray image, and predicting the one or more phenotypic traits based on one or more visual features of the colorized version of the X-ray image.

Colorizing x-ray images

Implementations are directed to generating a stream of agricultural annotations with respect to area(s) of interest of an agricultural field, and providing the stream of agricultural annotations for presentation to the user in an augmented reality manner with respect to the area(s) of interest. In some implementations, a stream of vision data may be received at a first computing device of the user and from a second computing device of the user. Further, the first computing device may process the stream of vision data to generate the stream of agricultural annotations. Moreover, the first computing device may transmit the stream of agricultural annotations to the second computing device to cause the stream of agricultural annotations to be provided for presentation to the user. In other implementations, the first computing device may be omitted, and the second computing device may be utilized to generate the stream of agricultural annotations.

Method(s) and system(s) for enabling augmented reality scouting of an agricultural field

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.

Individual plant recognition and localization

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.

Coordinating agricultural robots

Implementations are disclosed for adaptively adjusting various parameters of equipment in unpredictable terrain, such as agricultural fields. In various implementations, edge computing device(s) may obtain a first image captured by vision sensor(s) transported across an agricultural field by a vehicle. The first image may depict plant(s) growing in the agricultural area. The edge computing device(s) may process the first image based on a machine learning model to generate agricultural inference(s) about the plant(s) growing in the agricultural area. The edge computing device(s) may determine a quality metric for the agricultural inference(s). While the vehicle continues to travel across the agricultural field, and based on the quality metric: the edge computing device(s) may trigger one or more hardware adjustments to one or more of the vision sensors, or one or more adjustments in an operation of the vehicle.

Adaptively adjusting parameters of equipment operating in unpredictable terrain

Implementations are described herein for auditing performance of large-scale tasks. In various implementations, one or more ground-level vision sensors may capture a first set of one or more images that depict an agricultural plot prior to an agricultural task being performed in the agricultural plot, and a second set of one or more images that depict the agricultural plot subsequent to the agricultural task being performed in the agricultural plot. The first and second sets of images may be processed in situ using edge computing device(s) based on a machine learning model to generate respective pluralities of pre-task and post-task inferences about the agricultural plot. Performance of the agricultural task may include comparing the pre-task inferences to the post-task inferences to generate operational metric(s) about the performance of the agricultural task in the agricultural plot. The operational metric(s) may be presented at one or more output devices.

Auditing task performance

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.

Applying and using fiducial markings on agricultural apparatuses

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.

Generating crop yield predictions of geographic areas

Implementations are described herein for predicting soil organic carbon ('SOC') content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.

Predicting soil organic carbon content

Growing community of inventors

Woodside, CA, United States of America

Elliott Grant