Application of Tswin-F network based on multi-scale feature fusion in tomato leaf lesion recognition

Yuanbo Ye, Houkui Zhou, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

Abstract: Highlights•A new recognition model based on transformer architecture is proposed. Self-supervised learning is introduced to strengthen the modeling ability of long-distance relationships in images.•Use bilateral attention mechanisms to further strengthen the link of continuous information on images.•An FFLCA (Feature fusion local Attention) is proposed, which fuses the feature map output of the lower layer to the upper level, and fuses the feature information from the bottom to the upper level, which enhances the modeling ability of the entire network module and makes effective use of the global information.•A new parameter adjustment strategy is proposed, which combines flooding operation and dynamic attenuation of weights to improve the generalization ability of the model.