ESTIMATING PERFORMANCE AND SELECTING OPERATING PARAMETERS FOR A FARMING MACHINE USING A CALIBRATION PASS

    公开(公告)号:US20250000015A1

    公开(公告)日:2025-01-02

    申请号:US18217296

    申请日:2023-06-30

    Abstract: A method for calibrating performance characteristics of a farming machine using a performance report is described. The farming machine accesses images of plants in a field captured during the calibration pass. The images are input into a performance model to generate a performance report by identifying plants in the images using a plurality of identification sensitivities and determining expected performance characteristics of the farming machine for each of the identification sensitivities. As such, the performance report includes expected performance characteristics for each identification sensitives. The farming machine accesses a target performance characteristic (e.g., from an operator) for the farming machine corresponding identification sensitivity. Images are input into a plant identification model during a treatment pass which identify a plant in the field using the identification sensitivity corresponding to the target performance characteristic. The farming machine treats the plant in the field using a treatment array of the farming machine.

    AUTOMATED PLANT DETECTION USING IMAGE DATA

    公开(公告)号:US20210406540A1

    公开(公告)日:2021-12-30

    申请号:US17378658

    申请日:2021-07-17

    Abstract: 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.

    IDENTIFYING AND AVOIDING OBSTRUCTIONS USING DEPTH INFORMATION IN A SINGLE IMAGE

    公开(公告)号:US20210264624A1

    公开(公告)日:2021-08-26

    申请号:US17314218

    申请日:2021-05-07

    Abstract: A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses an image captured by the one or more sensors and identifies a distance value associated with each pixel of the image. The distance value corresponds to a distance between a point and an object that the pixel represents. The control system classifies pixels in the image as crop, plant, ground, etc. based on depth information in in the pixels. The control system generates a labelled point cloud using the labels and depth information, and identifies features about the crops, plants, ground, etc. in the point cloud. The control system generates treatment actions based on any of the depth information, visual information, point cloud, and feature values. The control system actuates a treatment mechanism based on the classified pixels.

    Plant group identification
    5.
    发明授权

    公开(公告)号:US11580718B2

    公开(公告)日:2023-02-14

    申请号:US16995618

    申请日:2020-08-17

    Abstract: A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

    Identifying and treating plants using depth information in a single image

    公开(公告)号:US11367207B2

    公开(公告)日:2022-06-21

    申请号:US17033263

    申请日:2020-09-25

    Abstract: A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses an image captured by the one or more sensors and identifies a distance value associated with each pixel of the image. The distance value corresponds to a distance between a point and an object that the pixel represents. The control system classifies pixels in the image as crop, plant, ground, etc. based on the visual information in the pixels. The control system generates a labelled point cloud using the labels and depth information, and identifies features about the crops, plants, ground, etc. in the point cloud. The control system generates treatment actions based on any of the depth information, visual information, point cloud, and feature values. The control system actuates a treatment mechanism based on the classified pixels.

    Automated plant detection using image data

    公开(公告)号:US11093745B2

    公开(公告)日:2021-08-17

    申请号:US15975092

    申请日:2018-05-09

    Abstract: 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.

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