Classification and localization of maize leaf spot disease based on weakly supervised learning

Frontiers in Plant Science

Plant Science

Yu Yao¹  Hengbin Wang¹  Runda Zhang¹  Yuanyuan Zhao¹  Xiaodong Zhang¹  Zhe Liu¹  Ziyao Xing¹  Shuai Yang¹  Xiang Gao¹  Xinrui Dong¹  Shaoming Li¹ 

[1] College of Land Science and Technology, China Agricultural University, Beijing, China;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China;

关键词: deep learning;    crop diseases;    interpretable AI;    image classification;    weakly supervised learning;   

DOI  :  10.3389/fpls.2023.1128399

 received in 2022-12-20, accepted in 2023-04-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Precisely discerning disease types and vulnerable areas is crucial in implementing effective monitoring of crop production. This forms the basis for generating targeted plant protection recommendations and automatic, precise applications. In this study, we constructed a dataset comprising six types of field maize leaf images and developed a framework for classifying and localizing maize leaf diseases. Our approach involved integrating lightweight convolutional neural networks with interpretable AI algorithms, which resulted in high classification accuracy and fast detection speeds. To evaluate the performance of our framework, we tested the mean Intersection over Union (mIoU) of localized disease spot coverage and actual disease spot coverage when relying solely on image-level annotations. The results showed that our framework achieved a mIoU of up to 55.302%, indicating the feasibility of using weakly supervised semantic segmentation based on class activation mapping techniques for identifying disease spots in crop disease detection. This approach, which combines deep learning models with visualization techniques, improves the interpretability of the deep learning models and achieves successful localization of infected areas of maize leaves through weakly supervised learning. The framework allows for smart monitoring of crop diseases and plant protection operations using mobile phones, smart farm machines, and other devices. Furthermore, it offers a reference for deep learning research on crop diseases.

【 授权许可】

Unknown   
Copyright © 2023 Yang, Xing, Wang, Gao, Dong, Yao, Zhang, Zhang, Li, Zhao and Liu