Rice Leaf Diseases Classification Using CNN with Transfer Learning

Rice is a staple crop in India, significantly contributing to food security and agricultural economies. However, rice cultivation faces substantial challenges due to various diseases that affect crop yield, particularly during the Kharif season (June-October), when warm and humid conditions favor pathogen proliferation. Manual disease identification by farmers is often inaccurate due to limited expertise, leading to improper disease management and yield losses. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), offer promising solutions for automated disease detection. This study proposes a CNN-based model leveraging Transfer Learning (TL) with a VGG16 architecture to classify rice leaf diseases efficiently. Due to the scarcity of publicly available datasets, we curated a custom dataset comprising images collected from rice fields and online sources. Despite the dataset’s limited size, transfer learning enabled robust feature extraction and improved classification performance. The model was trained and evaluated on real-world images, demonstrating high accuracy in identifying common rice leaf diseases. Our findings highlight the potential of deep learning in precision agriculture, providing farmers with an accessible tool for early disease detection and mitigation. The proposed approach can be extended to other crops, contributing to sustainable agricultural practices