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Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism Ma, Li; Yu, Qiwen; Yu, Helong; Zhang, Jian (Professor of biology)

Abstract

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications.

Item Metadata

Title
Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism
Creator
Ma, Li; Yu, Qiwen; Yu, Helong; Zhang, Jian (Professor of biology)
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2023-02-11
Description
Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications.
Subject
deep learning; attention mechanism; maize leaf disease; digital agriculture
Genre
Article
Type
Text
Language
eng
Date Available
2025-06-12
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0449107
URI
http://hdl.handle.net/2429/91343
Affiliation
Science, Irving K. Barber Faculty of (Okanagan); Non UBC; Biology, Department of (Okanagan)
Citation
Agronomy 13 (2): 521 (2023)
Publisher DOI
10.3390/agronomy13020521
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0