Sep 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 46 references
TL;DR
The proposed IFF fusion framework consistently maintains the performance of individual backbones as well as conventional fusion strategies, providing a generalizable feature calibration suitable for practical agricultural deployment.
Overall, MEAFNet provides an effective framework for automated grape leaf disease classification and offers a promising foundation for intelligent disease monitoring in precision viticulture.
Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui et al.· Artificial Intelligence and...· 0 citations
As the number of smart agricultural automation systems increases, accurate disease diagnosis is essential to maximize production efficiency. However, early-stage symptoms of strawberry diseases (e.g., Tipburn, Phytophthora blight, and Fusarium wilt) often manifest as low-contrast micro-symptoms that are visually simila...
Hye-Young Wang, Seoung-Ho Choi· Frontiers in Plant Science· 0 citations
Tomato leaf diseases can significantly reduce crop yield and quality, creating a need for accurate and efficient automated diagnostic systems. This paper presents LiteAttNet, a lightweight attention-enhanced framework for tomato leaf disease classification. The proposed architecture integrates a MobileNetV3Large backbo...
Alok Kumar Sharma, Yung-Fa Huang, Tan-Hsu Tan et al.· International Conference on...· 0 citations
A hybrid deep learning framework based on Data-efficient Image Transformers (DeiT) for plant disease classification and severity estimation that improves classification accuracy, enhances model transparency, and provides meaningful disease severity estimation is proposed, making it a promising solution for intelligent...
Tomato foliar diseases reduce yield, degrade fruit quality, and cause major economic losses, especially in regions such as Nigeria where diagnosis still relies largely on manual visual inspection. Although deep learning has improved automated plant disease recognition, many models are trained on controlled datasets tha...
Olayiwola Charles Adesoba, W. Apena, F. Dahunsi et al.· AI Systems Engineering· 0 citations
This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
Yu-Yan Xu, H. Chen, Qing-Mei Lin· Legume Research An Internati...· 0 citations
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