Research on Tomato Leaf Disease Detection and Recognition Based on Improved DeepLab V3+Model
Accurate identification of tomato leaf diseases is essential for tomato disease control.A multi-category segmentation mod-el of tomato leaf diseases based on the improved DeepLab V3+model was proposed to improve the accuracy of tomato disease con-trol.To improve the features expressive ability,a self-attention module was introduced after the output feature map of the backbone.A feature fusion strategy based on multi-level channel attention was also used,which captures the correlation between channels through global pooling and improves the information alignment problem during feature fusion at different levels.Through experimen-tal verification on the tomato leaf disease dataset,the average pixel accuracy and average intersection and union ratio of the im-proved model have been improved to a certain extent,which demonstrates the effectiveness of the improvement.
万方数据
维普
