Deep Learning-based Rice Leaf Disease Diagnosis using Convolutional Neural Networks

Abstract

Rice, a nutritious grain belonging to the Orza family, is a staple food for more than three billion people worldwide. Early detection of rice leaf disease is critical for maintaining healthy crops and ensuring food security. However, existing disease detection models have demonstrated low accuracy. This study proposes a Convolutional Neural Network (CNN) framework to detect rice leaf diseases more accurately. The framework involves preprocessing, segmentation, and classification of rice leaf images, implemented using Python. The proposed CNN model's performance is evaluated in terms of accuracy, precision, and recall. Results show significant improvement in accuracy compared to the existing models, indicating the potential of the proposed framework to enhance rice disease detection efforts.