MobiTran-SE: Hybrid MobileNetV3Small-Transformer architecture with squeeze-and-excitation for tomato leaf disease classification
Year: 2025
Type: article
Abstract: Diseases affecting tomato leaves represent a major risk to worldwide agricultural output and overall food security. In this study, we propose a innovative, lightweight and efficient deep learning (DL) approach for the classification of tomato leaf disease. Our architecture integrates the MobileNetV3Small backbone to extract multi-level features from input images, while Squeeze-and-Excitation (SE) blocks strengthen the focus on channel-wise features. A key component of our model is the incorporation of a Transformer-based module, which is applied to the fused features to extract long-range spatial interactions and contextual relationships. This hybrid approach enables the model to better distinguish between complex disease patterns in categories. The experimental findings indicate that the proposed model attains a high classification accuracy of 99.02%. The model also exhibits fast convergence and strong generalization, making it highly applicable for real-time deployment and resource-constrained agricultural environments. This work contributes a powerful and efficient solution to intelligent plant disease monitoring in the field of precision agriculture.
Authors: Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui, Huan Lam Le, Phuc Pham Tien, Hieu Nguyen Trung, Vuong Nguyen Phuc
Institution: FPT University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4415267920
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