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    ULTRAVIOLET LIGHT AND MACHINE LEARNING-BASED ASSESSMENT OF FOOD ITEM QUALITY
    92.
    发明公开
    ULTRAVIOLET LIGHT AND MACHINE LEARNING-BASED ASSESSMENT OF FOOD ITEM QUALITY 审中-公开

    公开(公告)号：US20240062265A1

    公开(公告)日：2024-02-22

    申请号：US18499544

    申请日：2023-11-01

    Applicant: Apeel Technology, Inc.

    Inventor： Benjamin Gordon ,  Ohad Michel ,  Matthew Kahlscheuer ,  Benjamin Flores ,  Charles Frazier ,  Louis Perez

    IPC: G06Q30/0601 ,  G06N20/00

    CPC classification number: G06Q30/0627 ,  G06N20/00

    Abstract: The disclosed technology provides for determining infection in food items using image data of the food items under ultra-violet (UV) light. A method includes performing object detection on the image data to identify a bounding box around each of the food items in the image data, determining, for each food item, an infection presence metric by applying a model to the bounding box, the model being trained using image training data of other food items under UV light, the image training data being annotated based on previous identifications of a first portion of the other food items having infection features and a second portion having healthy quality features, and determining, based on a determination that the infection presence metric for each of the food items indicates presence of an infection, an infection coverage metric for the food item.

    HYPERSPECTRAL IMAGE COMPRESSION USING A FEATURE EXTRACTION MODEL
    98.
    发明申请
    HYPERSPECTRAL IMAGE COMPRESSION USING A FEATURE EXTRACTION MODEL 有权

    公开(公告)号：US20220270298A1

    公开(公告)日：2022-08-25

    申请号：US17667151

    申请日：2022-02-08

    Applicant: Apeel Technology, Inc.

    Inventor： Richard Pattison

    IPC: G06T9/00 ,  G06V10/58

    Abstract: Disclosed are techniques for obtaining tensor data representing a hyperspectral image including a first portion depicting an object and a second portion depicting at least a portion of a surrounding environment where the object is located, identifying, by one or more computers, a portion of the tensor data representing the hyperspectral image that corresponds to the first portion of the hyperspectral image, providing, by the computers, the identified portion of the tensor data representing the hyperspectral image as an input to a feature extraction model; obtaining, by the computers, one or more matrix structures as output by the feature extraction model based on the feature extraction model processing the identified portion of the tensor data, the one or more matrix structures representing a subset of features extracted from the identified portion of the tensor data, and storing, by the computers, the one or more matrix structures in a memory device.

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