Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations

Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Seong Seok Choi, Yong Bae Seo and Gun-Do Kim ()
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Yong-Suk Lee: Department of Microbiology, Pukyong National University, Busan 48513, Republic of Korea
Maheshkumar Prakash Patil: Industry University Cooperation Foundation, Pukyong National University, Busan 48513, Republic of Korea
Jeong Gyu Kim: Department of Microbiology, Pukyong National University, Busan 48513, Republic of Korea
Seong Seok Choi: Department of Microbiology, Pukyong National University, Busan 48513, Republic of Korea
Yong Bae Seo: Department of Microbiology, Pukyong National University, Busan 48513, Republic of Korea
Gun-Do Kim: Department of Microbiology, Pukyong National University, Busan 48513, Republic of Korea

Agriculture, 2024, vol. 14, issue 9, 1-18

Abstract: To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules and Bidirectional Feature Pyramid Network (BiFPN) architecture. The C3NN modules were designed by integrating several soft attention modules into the C3 module: the Convolutional Block Attention Module (CBAM), Squeeze and Excitation Network (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA). The C3 modules in the Backbone and Head of YOLOv5 model were replaced with the C3NN to improve feature representation and object detection accuracy. The BiFPN architecture was implemented in the Neck of the YOLOv5 model to effectively merge multi-scale features and improve the accuracy of object detection. Among the various combinations for the improved YOLOv5m model, the C3ECA-BiFPN-C3ECA-YOLOv5m achieved a precision (P) of 87.764%, a recall (R) of 87.201%, an F1 of 87.482, an mAP.5 of 90.401%, and an mAP.5:.95 of 68.803%. In comparison with the YOLOv5m and Faster-RCNN models, the improved models showed improvement in P by 1.36% and 7.80%, R by 4.99% and 5.51%, F1 by 3.18% and 6.86%, mAP.5 by 1.74% and 2.90%, and mAP.5:.95 by 3.26% and 4.84%, respectively. These results demonstrate that the improved models have effective tomato leaf disease recognition capabilities and are expected to contribute significantly to the development of plant disease detection technology.

Keywords: tomato; Solanum lycopersicum L.; plant disease recognition; YOLOv5m; soft attention module; precision agriculture; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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