Author
Listed:
- Yange Sun
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Henan Key Laboratory of Tea Plant Biology, Xinyang 464000, China)
- Mingyi Jiang
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
- Huaping Guo
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
- Li Zhang
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
- Jianfeng Yao
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
- Fei Wu
(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
- Gaowei Wu
(State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China)
Abstract
Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the backbone network and fuse these features through the neck for accurate disease detection. This paper proposes a novel tea disease detection method that enhances feature expression of the backbone network and the feature fusion capability of the neck: (1) constructing an inverted residual self-attention module as a backbone plugin to capture the long-distance dependencies of disease spots on the leaves; and (2) developing a channel–spatial attention module with residual connection in the neck network to enhance the contextual semantic information of fused features in disease images and eliminate complex background noise. For the second step, the proposed channel–spatial attention module uses Residual Channel Attention (RCA) to enhance inter-channel interactions, facilitating discrimination between disease spots and normal leaf regions, and employs spatial attention (SA) to enhance essential areas of tea diseases. Experimental results demonstrate that the proposed method achieved accuracy and mAP scores of 92.9% and 94.6%, respectively. In particular, this method demonstrated improvements of 6.4% in accuracy and 6.2% in mAP compared to the SSD model.
Suggested Citation
Yange Sun & Mingyi Jiang & Huaping Guo & Li Zhang & Jianfeng Yao & Fei Wu & Gaowei Wu, 2024.
"Multiscale Tea Disease Detection with Channel–Spatial Attention,"
Sustainability, MDPI, vol. 16(16), pages 1-14, August.
Handle:
RePEc:gam:jsusta:v:16:y:2024:i:16:p:6859-:d:1453434
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