Blue River Technology Inc.

Deere + Company

Rajesh Radhakrishnan

Lee Kamp Redden

James Patrick Ostrowski

Thomas W Brown

Edward William Jeffcott Pell

Christopher Grant Padwick

Rahul Agrawal

Brent William West

Deaglan Halligan

John William Peake

A control system of a farming machine is configured to identify obstruction in a field from image data of the field. The control system accesses an obstruction model configured to identify obstructions in a field from image data of the field. The obstruction model is generated by accessing image data of obstructions in a training field, each obstruction corresponding to a prescribed action occurring at a prescribed time, labelling the image data of the obstructions, and training the obstruction model based on the labelled image data. The control system captures image data of the field including an obstruction and inputs the image data into the obstruction model. Responsive to identifying the obstruction in the field, the control system modifies treatment instructions of the farming machine such that the farming machine performs a implements a farming objective while avoiding the obstruction in the field.

Machine-learned obstruction detection in a farming machine

A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Plant identification using dynamic cropping

A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.

Automated plant detection using image data

A plant treatment system automatically adjusts camera operation parameters for a camera used by the plant treatment system to identify and treat plants in a field. The plant treatment system can generate image segments of images received from the camera and classify the image segments based on whether the image segments represent plants. The plant treatment system determines whether each of the image segments is over- or under-exposed and adjusts the camera operation parameters for the camera based on the exposure classification of the image segments. Alternatively, the plant treatment system may use a plant detection model to identify plant pixels within an image that represent plants. The plant treatment system can then determine whether the identified plant pixels are over- or under-exposed and adjust the camera operation parameters accordingly.

Automatic camera parameter adjustment on a plant treatment system

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Mountain View, CA, United States of America

Rajesh Radhakrishnan