Weakly supervised localization model for plant disease based on Siamese networks.

Problems: Plant diseases significantly impact crop growth and yield. The variability and unpredictability of symptoms postinfection increase the complexity of image-based disease detection methods, leading to a higher false alarm rate.

Aim: To address this challenge, we have developed an efficient, weakly supervised agricultural disease localization model using Siamese neural networks.

Methods: This model innovatively employs a Siamese network structure with a weight-sharing mechanism to effectively capture the visual differences in plants affected by diseases. Combined with our proprietary Agricultural Disease Precise Localization Class Activation Mapping algorithm (ADPL-CAM), the model can accurately identify areas affected by diseases, achieving effective localization of plant diseases.

Results And Conclusion: The results showed that ADPL-CAM performed the best on all network architectures. On ResNet50, ADPL-CAM's top-1 accuracy was 3.96% higher than GradCAM and 2.77% higher than SmoothCAM; the average Intersection over Union (IoU) is 27.09% higher than GradCAM and 19.63% higher than SmoothCAM. Under the SPDNet architecture, ADPL-CAM achieves a top-1 accuracy of 54.29% and an average IoU of 67.5%, outperforming other CAM methods in all metrics. It can accurately and promptly identify and locate diseased leaves in crops.