Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM - Zhang, Xiaoqian; Li, Dongming; Liu, Xuan; Sun, Tao; Lin, Xiujun; Ren, Zhenhui (Frontiers in Plant Science, 2023)
Identification technology of apple diseases is of great significance in improving production efficiency and quality. This paper has used apple Alternaria blotch and brown spot disease leaves as the research object and proposes a disease spot segmentation and disease identification method based on DFL-UNet+CBAM to address the problems of low recognition accuracy and poor performance of small spot segmentation in apple leaf disease recognition. The goal of this paper is to accurately prevent and control apple diseases, avoid fruit quality degradation and yield reduction. DFL-UNet+CBAM model has employed a hybrid loss function of Dice Loss and Focal Loss as the loss function and added CBAM attention mechanism to both effective feature layers extracted by the backbone network and the results of the first upsampling, enhancing the model to rescale the inter-feature weighting relationships, enhance the channel features of leaf disease spots and suppressing the channel features of healthy parts of the leaf. After training, the average loss rate of the improved model decreases from 0.063 to 0.008 under the premise of ensuring the accuracy of image segmentation. The smaller the loss value is, the better the model is. In the lesion segmentation and disease identification test, MIoU was 91.07%, MPA was 95.58%, F1 Score was 95.16%, they increased by 1.96%, 1.06% and 1.14% respectively. Specifically, compared to the original U-Net model, the segmentation of Alternaria blotch disease, MIoU, MPA, Precision, Recall and the F1 Score increased by 4.41%, 4.13%, 1.49%, 4.13%, 2.81%; in the segmentation of brown spots, MIoU, MPA, Precision, Recall and the F1 Score increased by 1.18%, 0.6%, 0.78%, 0.6%, 0.69%, respectively. Because brown spot spots are larger than Alternaria blotch spots. The segmentation performance of smaller disease spots has increased more noticeably, according to the quantitative analysis results, proving that the model's capacity to segment smaller disease spots has greatly improved. The model in this paper can obtain more sophisticated semantic information in comparison to the traditional U-Net, further enhance the recognition accuracy and segmentation performance of apple leaf spots,and address the issues of low accuracy and low efficiency of conventional disease recognition methods.