Paper Title
CNN-Based Classification of Rice Leaf Diseases and Deployment in a Web Environment
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Registration ID: IJNRD_306712
Published ID: IJNRD2505144
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Keywords
Rice Leaf Disease Detection, Deep Learning, ResNet152, Transfer Learning, Flask Web Application, Precision Agriculture, Image Classification, Grad-CAM, Mobile Deployment.
Abstract
The timely and accurate detection of rice leaf diseases is critical for safeguarding global food security, as rice is a primary staple crop for over half the world's population. Traditional diagnostic methods rely on manual inspection, which can be error-prone, inconsistent, and infeasible at scale. This research introduces a deep learning-based method for automatically classifying rice leaf diseases, utilizing a customized and fine-tuned ResNet152 architecture.The model was trained on a curated dataset of 2,627 labeled images spanning six distinct disease categories, utilizing transfer learning and targeted fine-tuning to optimize performance. The model achieved a test accuracy of 95.2%, with an average F1-score of 0.92, demonstrating strong generalization despite class imbalance. In addition to core model development, we deploy the trained model via a scalable Flask-based web application, enabling real-time disease prediction through a simple image upload interface. To enhance usability in real-world agricultural scenarios, the system supports batch inference, robust image preprocessing, and user feedback integration. This work not only demonstrates the potential of convolutional neural networks in precision agriculture but also offers a practical and accessible tool for disease monitoring, potentially aiding farmers and agronomists in making informed crop management decisions. Future enhancements include mobile deployment using TensorFlow Lite, integration of explainable AI tools such as Grad-CAM for model transparency, and expansion to additional crop species and disease categories.
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How To Cite (APA)
Twinkal D. Solanki & Maulik D. Trivedi (May-2025). CNN-Based Classification of Rice Leaf Diseases and Deployment in a Web Environment. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(5), b433-b439. https://ijnrd.org/papers/IJNRD2505144.pdf
Issue
Volume 10 Issue 5, May-2025
Pages : b433-b439
Other Publication Details
Paper Reg. ID: IJNRD_306712
Published Paper Id: IJNRD2505144
Research Area: Science and Technology
Author Type: Indian Author
Country: Amreli, Gujrat, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2505144.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2505144
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