Grape leaf disease detection based on attention mechanisms
نویسندگان
چکیده
Prevention and control of grape diseases is the key measure to ensure yield. In order improve precision leaf disease detection, in this study, Squeeze-and-Excitation Networks (SE), Efficient Channel Attention (ECA), Convolutional Block Module (CBAM) attention mechanisms were introduced into Faster Region-based Neural (R-CNN), YOLOx, single shot multibox detector (SSD), enhance important features weaken unrelated real-time performance model improving its detection precision. The study showed that R-CNN, SSD models based on different effectively enhanced operation speed by slightly enhancing parameters. Optimal among three types selected for comparison, results R-CNN+SE had lower precision, YOLOx+ECA required least parameters with highest SSD+SE optimal relatively high This solved problem difficulty provided a reference analysis symptoms automated agricultural production. Keywords: SSD, mechanism DOI: 10.25165/j.ijabe.20221505.7548 Citation: Guo W J, Feng Q, Li X Z, Yang S, J Q. Grape mechanisms. Int Agric & Biol Eng, 2022; 15(5): 205–212.
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ژورنال
عنوان ژورنال: International Journal of Agricultural and Biological Engineering
سال: 2022
ISSN: ['1934-6352', '1934-6344']
DOI: https://doi.org/10.25165/j.ijabe.20221505.7548