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    SYSTEM AND METHOD FOR DETERMINING DAMAGE ON CROPS
    11.
    发明申请
    SYSTEM AND METHOD FOR DETERMINING DAMAGE ON CROPS 有权

    公开(公告)号：US20230017425A1

    公开(公告)日：2023-01-19

    申请号：US17779819

    申请日：2020-11-24

    Applicant: BASF SE

    Inventor： Aranzazu Bereciartua-Perez ,  Artzai Picon Ruiz ,  Javier Romero Rodriguez ,  Juan Manuel Contreras Gallardo ,  Rainer Oberst ,  Hikal Khairy Shohdy Gad ,  Gerd Kraemer ,  Christian Klukas ,  Till Eggers ,  Jone Echazarra Huguet ,  Ramon Navarra-Mestre

    IPC: G06T7/00 ,  G06T3/40 ,  G06V10/82 ,  G06T7/11 ,  G06V20/70 ,  G06V10/776 ,  G06V10/774 ,  G06V20/10

    Abstract: A computer-implemented method, computer program product and computer system (100) for determining the impact of herbicides on crop plants (11) in an agricultural field (10). The system includes an interface (110) to receive an image (20) with at least one crop plant representing a real world situation in the agricultural field (10) after herbicide application. An image pre-processing module (120) rescales the received image (20) to a rescaled image (20a) matching the size of an input layer of a first fully convolutional neural network (CNN1) referred to as the first CNN. The first CNN is trained to segment the rescaled image (20a) into crop (11) and non-crop (12, 13) portions, and provides a first segmented output (20s1) indicating the crop portions (20c) of the rescaled image with pixels belonging to representations of crop. A second fully convolutional neural network (CNN2), referred to as the second CNN, is trained to segment said crop portions into a second segmented output (20s2) with one or more sub-portions (20n, 20l) with each sub-portion including pixels associated with damaged parts of the crop plant showing a respective damage type (11-1, 11-2). A damage measurement module (130) determines a damage measure (131) for the at least one crop plant for each damage type (11-1, 11-2) based on the respective sub-portions of the second segmented output (20s2) in relation to the crop portion of the first segmented output (20s1).

    HERBICIDAL MIXTURES
    13.
    发明申请
    HERBICIDAL MIXTURES 有权

    公开(公告)号：US20210037824A1

    公开(公告)日：2021-02-11

    申请号：US16966990

    申请日：2019-02-04

    Applicant: BASF SE

    Inventor： Matthias Witschel ,  Ryan Louis Nielson ,  Gerd Kraemer

    IPC: A01N43/84 ,  A01N25/12 ,  A01N43/80

    Abstract: The present invention relates to mixture A comprising trifludimoxazin and 2-(2,4-dichlorophenyl)methyl-4,4-di-methyl-3-isoxazolidone defined below and to their use as herbicide. Moreover, the invention relates to compositions for crop protection and to a method for controlling unwanted vegetation.

    System and method for identification of plant species
    16.
    发明授权
    System and method for identification of plant species 有权

    公开(公告)号：US12142033B2

    公开(公告)日：2024-11-12

    申请号：US17640742

    申请日：2020-09-03

    Applicant: BASF SE

    Inventor： Artzai Picon Ruiz ,  Miguel Linares De La Puerta ,  Christian Klukas ,  Till Eggers ,  Rainer Oberst ,  Juan Manuel Contreras Gallardo ,  Javier Romero Rodriguez ,  Hikal Khairy Shohdy Gad ,  Gerd Kraemer ,  Jone Echazarra Huguet ,  Ramon Navarra-Mestre ,  Miguel Gonzalez San Emeterio

    IPC: G06V10/82 ,  G06T7/00 ,  G06V20/10

    Abstract: A computer-implemented method, computer program product and computer system (100) for identifying weeds in a crop field using a dual task convolutional neural network (120) having a topology with an intermediate module (121) to execute a classification task being associated with a first loss function (LF1), and with a semantic segmentation module (122) to execute a segmentation task with a second different loss function (LF2). The intermediate module and the segmentation module are being trained together, taking into account the first and second loss functions (LF1, LF2). The system executes a method including receiving a test input (91) comprising an image showing crop plants of a crop species in an agricultural field and showing weed plants of one or more weed species among said crop plants; predicting the presence of one or more weed species (11, 12, 13) which are present in the respective tile; outputting a corresponding intermediate feature map to the segmentation module as output of the classification task; generating a mask for each weed species class as segmentation output of the second task by extracting multiscale features and context information from the intermediate feature map and concatenating the extracted information to perform semantic segmentation; and generating a final image (92) indicating for each pixel if it belongs to a particular weed species, and if so, to which weed species it belongs.

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