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This method introduces a multi-scale convolution attention module (MSAM) in the feature extraction network to enhance the focus on grape leaf disease. By performing convolution operations on features at different scales and using attention mechanism to emphasize the features of the diseased area, the model can better capture the subtle features of the disease. Experimental results show that MSAM-YOLO improves the original model by 4% in grape leaf disease identification task, with higher accuracy and real-time performance. 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