International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 4
July-August 2025

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Automated Rice Disease Detection using CNN and Vision Transformer-based Frameworks

Author(s) Ms. Sumaiya Tasnim, Dr. Kamrul Hasan Talukder
Country Bangladesh
Abstract Rice serves as the primary staple food for more than 160 million individuals in Bangladesh, contributing significantly to the nation's economy and food security. Nevertheless, rice farming is significantly challenged by a range of diseases, including bacterial leaf blight, brown spot, and leaf blast, which can result in considerable reductions in yield. Conventional methods for detecting diseases tend to require a lot of labor, consume considerable time, and depend heavily on the expertise of specialists, which limits their practicality for use by farmers in Bangladesh. This study explores the application of advanced deep learning architectures - ConvNeXt-Small, EfficientNet-B3, MobileNetV2, ResNet-50 and DeiT-Tiny for the identification of diseases affecting rice leaves using a dataset comprising approximately 12,700 images. By utilizing transfer learning methods, we seek to evaluate and contrast the effectiveness of these models in correctly detecting and categorizing rice leaf diseases. The findings of this study have the potential to advance effective and scalable methods for automating disease identification in rice farming, particularly in the context of developing countries' agricultural landscape.
Keywords Computer Vision, Rice Crop Disease Detection, Deep Learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-17
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.50925
Short DOI https://doi.org/g9tzz3
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E-ISSN 2582-2160
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