Multiscale Tea Disease Detection with Channel–Spatial Attention

Yange Sun (), Mingyi Jiang, Huaping Guo, Li Zhang, Jianfeng Yao, Fei Wu and Gaowei Wu
Additional contact information
Yange Sun: School of Computer and Information Technology, Xinyang Normal University, 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

Sustainability, 2024, vol. 16, issue 16, 1-14

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.

Keywords: tea disease detection; object detection; channel attention; spatial attention; feature fusion (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/16/6859/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/16/6859/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:16:p:6859-:d:1453434

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().