Mineral Earth Sciences LLC

Deere & Company

X Development LLC

Sergey Yaroshenko

Elliott Grant

Gabriella Levine

Zhiqiang Yuan

Benoit M Schillings

Jie Yang

Yueqi Li

Nanzhu Wang

Alan Eneev

Daniel Ribeiro Silva

Yujing Qian

Hongxiao Liu

Francis Ebong

Cameron Cruz

Matt VanCleave

Sicong Wang

Linda Kanu

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

Implementations are described for improving accuracy and efficiency in anomaly detection during autonomous scouting of crop fields. A method is provided and includes: operating an unmanned aerial vehicle (UAV) to fly along an initial flight path above a field, where the UAV collect images for the field and an edge-computing device disposed on (or included in) the UAV processes the images using a coarse-grained machine learning (ML) model to determine whether target region(s) are detected within the field. A reversed flight path can be determined based on the detected target region(s), and the UAV can fly along the reversed flight path from a point corresponding to an ending point of the initial flight path, to collect target images particularly for the detected target region(s). The collected target images can be processed using a fine-grained ML model, to more fully characterize the detected target region(s).

Autonomous scouting using unmanned aerial vehicle

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 disclosed for selectively operating edge-based sensors and/or computational resources under circumstances dictated by observation of targeted plant trait(s) to generate targeted agricultural inferences. In various implementations, triage data may be acquired at a first level of detail from a sensor of an edge computing node carried through an agricultural field. The triage data may be locally processed at the edge using machine learning model(s) to detect targeted plant trait(s) exhibited by plant(s) in the field. Based on the detected plant trait(s), a region of interest (ROI) may be established in the field. Targeted inference data may be acquired at a second, greater level of detail from the sensor while the sensor is carried through the ROI. The targeted inference data may be locally processed at the edge using one or more of the machine learning models to make a targeted inference about plants within the ROI.

Edge-based processing of agricultural data

Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.

Generating a local mapping of an agricultural field for use in performance of agricultural operation(s)

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.

Robotic agricultural remediation

Growing community of inventors

Newark, CA, United States of America

Sergey Yaroshenko