Blue River Technology Inc.

Deere & Company

United States of America As Represented by the Administrator of Nasa

Lee Kamp Redden

James Patrick Ostrowski

Edward William Jeffcott Pell

Kent M Anderson

Matthew Stephen Colgan

Rajesh Radhakrishnan

Nicholas Elia Apostoloff

Chia-Chun Fu

Olgert Denas

Chris Padwick

Andrei Polzounov

Thomas W Brown

Christopher Grant Padwick

Kent Michael Anderson

James Willis

Deaglan Halligan

Divya Sharma

Benjamin Ray Chostner

William Louis Patzoldt

Erik Ehn

John Chadwick Yagow

Bryon William Majusiak

Zebulon Elias Wheeler

Larry K Dungan

Paul S Valle

Derek R Bankieris

Asher P Lieberman

Cecil Shy

Zeb Wheeler

James Michael Fleming

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

System and method for automated odometry calibration for precision agriculture systems

A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

Semantic segmentation to identify and treat plants in a field and verify the plant treatments

A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

Detecting untraversable environment and preventing damage by a vehicle

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

Plant feature detection using captured images

A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Plant treatment based on morphological and physiological measurements

Embodiments relate to a system that generates a crop yield component map for a field. The system determines amounts of nitrogen applied to each portion of the field by a set of nitrogen applicator farming machines. The system accesses crop yield data associated with a crop that was grown in the field. The crop yield data was generated by a set of harvester farming machines that travelled through the field and harvested plant parts of the crop. The system determines, by analyzing the crop yield data, plant part metrics for the harvested plant parts in each field portion. The system generates a crop yield component map that maps, for each field portion, a plant part metric associated with the field portion and an amount of nitrogen applied to the field portion. The component map may then be provided for display.

Crop yield component map

A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.

Method and apparatus for automated plant necrosis

A method for plant treatment, including: receiving a first measurement for a plant from a sensor as the sensor moves within a geographic area comprising a plurality of plants; in response to receipt of the first measurement and prior to receipt of a second measurement for a second plant of the plurality, determining a set of treatment mechanism operation parameters for the plant to optimize a geographic area output parameter based on the first measurement and historical measurements for the geographic area; determining an initial treatment parameter for the plant; and operating a treatment mechanism in a treatment mode based on the set of operating parameters in response to satisfaction of the initial treatment parameter.

System and method for plant treatment based on neighboring effects

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 modular system includes a hub and a set of modules removably coupled to the hub. The modules are physically coupled to the frame relative to each other so that each module can operate with respect to a different row of a field. An individual module includes a sensor for capturing field measurement data of individual plants along a row as the modular system moves through the geographic region. An individual module further includes a treatment mechanism for applying a treatment to the individual plants of the row based on the field measurement data before the modular system passes by the individual plants. An individual module further includes a computing device that determines the treatment based on the field measurement data and communicates data to the hub. The hub is communicatively coupled to the modules, so that it may exchange data between the modules and with a remote computing system.

Modular precision agriculture system

Growing community of inventors

Palo Alto, CA, United States of America

Lee Kamp Redden