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Communication Dans Un Congrès Année : 2023

117. Sugar beet disease detection based on remote sensing data and artificial intelligence

Y. Lebrini

Résumé

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 an RGB sensor camera simulating real acquisition on a sprayer was performed. For dataset preparation, about 986 images were collected from a sugar beet field in North France under varying conditions. The accuracy assessment was performed using statistical indicators which are Precision, Recall, F1-score, and Average Precision. The Mask RCNN model detects 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.
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Dates et versions

hal-04182204 , version 1 (17-08-2023)

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Citer

Y. Lebrini, Alicia Ayerdi Gotor. 117. Sugar beet disease detection based on remote sensing data and artificial intelligence. 14th European Conference on Precision Agriculture, Jul 2023, Bologna, Italy. pp.933-938, ⟨10.3920/978-90-8686-947-3_117⟩. ⟨hal-04182204⟩
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