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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 |
doi: 10.3389/fpls.2024.1418201
This article is part of the Research Topic Innovative Approaches to Agricultural Plant Disease Identification: Integrating Deep Learning into Traditional Methods View all 9 articles
Weakly Supervised Localization Model for Plant Disease Based on Siamese Networks
Provisionally accepted- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
Problems:Plant diseases significantly impact crop growth and yield. The variability and unpredictability of symptoms post-infection 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 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.
Keywords: plant disease, deep learning, Siamese networks, Weakly supervised localization, Class activation mapping
Received: 16 Apr 2024; Accepted: 04 Sep 2024.
Copyright: © 2024 Chen, Guo, Zhang, Liang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jianwen Guo, School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
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