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- W4386868344 abstract "Based on the increasingly severe reality of land desertification, it is of great practical significance to improve the efficiency of vegetation restoration, and technologies such as artificial intelligence, automation, and information communication will greatly improve the efficiency of vegetation restoration. In this study, a unified platform was constructed to test four activation functions (ReLU, SiLU, GELU, and Mish) under the same conditions, based on research on different neural network functions and their application in the YOLOv5 model. Through analysis of the experimental results, it was found that the YOLOv5 model with the SiLU activation function performed well in plant disease recognition tasks and showed outstanding performance in multi-target recognition." @default.
- W4386868344 created "2023-09-20" @default.
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- W4386868344 date "2023-04-21" @default.
- W4386868344 modified "2023-09-27" @default.
- W4386868344 title "Identification of plant diseases based on yolov5" @default.
- W4386868344 cites W2102478133 @default.
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- W4386868344 doi "https://doi.org/10.1109/icsp58490.2023.10248628" @default.
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