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    System and method for identification of plant species
    2.
    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.

    LINEAR MODEL VALIDATION
    4.
    发明申请
    LINEAR MODEL VALIDATION 有权

    公开(公告)号：US20240428054A1

    公开(公告)日：2024-12-26

    申请号：US18684043

    申请日：2022-08-17

    Applicant: BASF SE

    Inventor： Iain Proctor ,  Eduard Szoecs ,  Georgios Kritikos ,  Till Eggers

    IPC: G06N3/0464

    Abstract: The present invention relates to validating a linear model. Input data is received (102) and the linear model to be validated is provided (104). Predicted data is determined based on processing input data by the linear model (106). Residual data is determined based on a difference between the predicted data and the input data (108). A set of validation data including homoscedasticity validation data or normality validation data is generated based on the residual data (110). A binary classifier is provided and used for determining whether the set of validation data fulfills a validation condition (112), namely a homoscedasticity condition or a normality condition. The binary classifier is a trained data driven model that outputs that the validation condition is fulfilled or not fulfilled depending on the set of validation data. Finally, it is determined whether the linear model is valid based on the output of the binary classifier (114).

    System and method for plant disease detection support
    5.
    发明授权
    System and method for plant disease detection support 有权

    公开(公告)号：US12148197B2

    公开(公告)日：2024-11-19

    申请号：US17611517

    申请日：2020-05-14

    Applicant: BASF SE

    Inventor： Artzai Picon Ruiz ,  Matthias Nachtmann ,  Maximilian Seitz ,  Patrick Mohnke ,  Ramon Navarra-Mestre ,  Alexander Johannes ,  Till Eggers ,  Amaia Maria Ortiz Barredo ,  Aitor Alvarez-Gila ,  Jone Echazarra Huguet

    IPC: G06V10/764 ,  G06N3/048 ,  G06T7/00 ,  G06T7/10 ,  G06V10/82 ,  G06V20/10

    Abstract: A computer-implemented method, computer program product and computer system (100) for detecting plant diseases. The system stores a convolutional neural network (120) trained with a multi-crop dataset. The convolutional neural network (120) has an extended topology comprising an image branch (121) based on a classification convolutional neural network for classifying the input images according to plant disease specific features, a crop identification branch (122) for adding plant species information, and a branch integrator for integrating the plant species information with each input image. The plant species information (20) specifies the crop on the respective input image (10). The system receives a test input comprising an image (10) of a particular crop (1) showing one or more particular plant disease symptoms, and further receives a respective crop identifier (20) associated with the test input via an interface (110). A classifier module (130) of the system applies the trained convolutional network (120) to the received test input, and provides a classification result (CR1) according to the output vector of the convolutional neural network (120). The classification result (CR1) indicates the one or more plant diseases associated with the one or more particular plant disease symptoms.

    SYSTEM AND METHOD FOR DETERMINING DAMAGE ON CROPS
    6.
    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).

    System and method for detecting plant diseases
    7.
    发明授权
    System and method for detecting plant diseases 有权

    公开(公告)号：US11037291B2

    公开(公告)日：2021-06-15

    申请号：US16300988

    申请日：2017-04-19

    Applicant: BASF SE

    Inventor： Alexander Johannes ,  Till Eggers ,  Artzai Picon ,  Aitor Alvarez-Gila ,  Amaya Maria Ortiz Barredo ,  Ana Maria Diez-Navajas

    IPC: G06K9/00 ,  G06T7/00 ,  G06T7/90 ,  G01N21/27 ,  G01N33/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11) of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster is associated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined threshold, by using a Bayes classifier that models visual feature statistics which are always present on a diseased plant image. A plant disease diagnosis module (150) configured to extract, by using a statistical inference method, from each candidate region (C4, C5, C6, Cn) one or more visual features to determine for each candidate region one or more probabilities indicating a particular disease; and to compute a confidence score (CS1) for the particular disease by evaluating all determined probabilities of the candidate regions (C4, C5, C6, Cn).

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