Islam, ApuRafi, Ishraque ArefinRahman, Somaya Al SadiaMondal S.Alam, Md. Golam Rabiul2026-10-052026-10-052025-01-01A. Islam, I. A. Rafi, S. A. S. Rahman, S. Mondal and M. G. R. Alam, "Rice Leaf Disease Classification: A Comparative Evaluation of CNNs and Vision Transformers," 2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 5537-5542, doi: 10.1109/ICCIT68739.2025.11490559.97983315786712-s2.0-105041620523https://hdl.handle.net/10361/30400Rice is a staple crop worldwide but is highly vulnerable to leaf diseases that threaten food security. Traditional manual inspection is slow, labor-intensive, and error-prone, highlighting the need for automated detection. This research introduces a comparative analysis between convolutional neural networks (CNNs) and vision transformers (ViTs) for multi-class rice leaf disease classification. Using an 11-class dataset of bacterial, fungal, viral, pest-related, and healthy leaves, we evaluated ResNet50, MobileNetV2, BEiT, DeiT, Swin Transformer V2, and Swin-Tiny V2. Results show transformer models outperform CNNs, with the quantized Swin-Tiny V2 Transformer achieving 99.56% accuracy while reducing model size to 26.89 MB, making it suitable for mobile and IoT deployment. To enhance interpretability and understand the model's decisions, Grad-CAM visualizations highlight important regions, ensuring transparency in model predictions. The findings demonstrate the potential of lightweight, explainable transformers for realworld agricultural IoT applications in precision farming.6 Pagesen-USRadio broadcastingFrequency modulationAI acceleratorsCentral Processing Unit (CPU)Microprocessor chipsInternet of thingsCommunication systemsRice leaf diseaseVision transformersConvolutional Neural Networks (CNN)Plant diseases--Diagnosis.Rice--Diseases and pests.Conference Proceeding10.1109/ICCIT68739.2025.11490559