ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1687300
This article is part of the Research TopicSmart Plant Pest and Disease Detection Machinery and Technology: Innovations for Sustainable AgricultureView all 3 articles
RTCB: An Integrated Deep Learning Model for Garlic Leaf Disease Identification
Provisionally accepted
- 1Kuala Lumpur University of Science and Technology, Kuala Lumpur, Malaysia
- 2Henan University of Urban Construction, Pingdingshan, China
- 3Henan Xinxiang Vocational College of Industry and Commerce, Xinxiang, China
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Abstract Problem: Garlic is a common ingredient that not only enhances the flavor of dishes but also has various beneficial effects and functions for humans. However, its leaf diseases and pests have a serious impact on the growth and yield. Traditional plant leaf disease detection methods have shortcomings, such as high time consumption and low recognition accuracy. Methodology: As a result, we present a deep learning approach based on an upgraded ResNet18, triplet, convolutional block (RTCB) attention mechanism for recognizing garlic leaf diseases. First, we replace the convolutional layers in the residual block with partial convolutions based on the classic ResNet18 architecture to improve computational efficiency. Then, we introduce triplet attention after the first convolutional layer to enhance the model's ability to focus on key features. Finally, we add a convolutional block attention mechanism after each residual layer to improve the model's feature perception. Results: The experimental results demonstrate that the proposed model achieves a classification accuracy of 98.90%, which is superior to outstanding deep learning models such as Efficient-v2-B0, MobileOne-S0, OverLoCK-S, EfficientFormer, and MobileMamba. The proposed RTCB has a faster computation speed, higher recognition precision, and stronger generalization ability. Contribution: The proposed approach provides a scalable technical reference for the engineering application of automatic disease monitoring and control in intelligent agriculture. The current strategy is conducive to the deployment of edge computing equipment and has extensive significance and application potential in plant leaf disease detection.
Keywords: agricultural production, Plant Leaf Disease Detection, deep learning, Improved ResNet18, attention mechanism
Received: 17 Aug 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Liu, Kan, Chen, Xu, Zheng, Ahmad and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junmin Zhao, zhaojunminhuuc@yeah.net
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