Rice Plant Disease Detection Using Efficient Net V2

Abstract

Rice is a staple crop feeding billions worldwide, yet its production is severely impacted by plant diseases, leading to significant economic losses and food insecurity. This project proposes an advanced Rice Plant Disease Detection System leveraging EfficientNetV2, a state-of the-art deep learning architecture, to achieve high accuracy in identifying and classifying rice diseases. The system incorporates geo-specific tagging during image acquisition, enabling location-based disease mapping and tailored crop recommendations. Key features include real-time disease detection, severity analysis, and actionable insights through a user-friendly dashboard with multilingual support. By addressing the limitations of traditional methods and existing automated solutions—such as overfitting, lack of scalability, and real-world adaptability—this project aims to provide an accurate, scalable, and accessible solution for farmers, ultimately promoting sustainable agriculture and enhancing rice crop yield.