RICE LEAF DISEASE DETECTION USING EFFICIENTNET

Abstract

Rice is a staple crop for a large portion of the global population, and its productivity is significantly affected by various leaf diseases. Early and accurate detection of these diseases is crucial to minimize crop loss and ensure food security. This study proposes an automated rice leaf disease detection system using EfficientNet, a state-of-the-art convolutional neural network architecture known for its high accuracy and computational efficiency. The system classifies rice leaf images into multiple disease categories by automatically extracting hierarchical features from the input images. Experimental results on standard rice leaf datasets demonstrate that the EfficientNet-based model achieves high precision and recall, outperforming traditional CNN architectures while maintaining lower computational costs. The proposed approach provides an effective and scalable solution for real-time agricultural disease monitoring, enabling timely interventions and improved crop management