TY - JOUR
AU - Ding, Jianhao
AB - This paper proposes an improved YOLO v8 grape leaf disease identification method MSAM-YOLO based on attention mechanism to address the problem of multiple types and similar small target features in grape leaf disease images. 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. This method provides a new perspective for the detection of plant leaf diseases, contributing to the improvement of the quality and efficiency of agricultural production.
TI - MSAM-YOLO: An improved YOLO v8 based on attention mechanism for grape leaf disease identification method
JO - Journal of Computational Methods in Science and Engineering
DO - 10.1177/14727978251323226
DA - 2025-05-01
UR - https://www.deepdyve.com/lp/ios-press/msam-yolo-an-improved-yolo-v8-based-on-attention-mechanism-for-grape-PmvNIzIRRB
SP - 2874
EP - 2882
VL - 25
IS - 3
DP - DeepDyve
ER -