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- W4382867126 abstract "Plant disease detection for precision chemicals application is a field of interest to reduce the use of chemicals in the field. Detection of diseases with a Red, Green, Blue (RGB) sensor camera simulating real acquisition on a sprayer was undertaken. For dataset preparation, about 986 images were collected from a sugar beet field in North France under varying conditions. The accuracy assessment was carried out using statistical indicators which are Precision, Recall, F1-score, and Average Precision. The mask Region-based Convolutional Neural Networks (RCNN) model detected sugar beet disease with an average precision of 92.73% for the three disease types. Artificial intelligence has a strong potential to reduce the use of chemicals thus costs optimization and environmental risks minimization over agricultural fields." @default.
- W4382867126 created "2023-07-02" @default.
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- W4382867126 date "2023-07-02" @default.
- W4382867126 modified "2023-09-23" @default.
- W4382867126 title "117. Sugar beet disease detection based on remote sensing data and artificial intelligence" @default.
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- W4382867126 doi "https://doi.org/10.3920/978-90-8686-947-3_117" @default.
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