Abstract

This research addresses the critical need for early crop disease detection to optimize yields and minimize economic losses. Leveraging DenseNet, a deep learning model, the study focuses on automated detection of rice leaf diseases. A mobile application is proposed, integrating a user-friendly interface and advanced image processing algorithms, empowering farmers to identify threats promptly. The system utilizes a pre-trained DenseNet model for realtime disease classification, offering instant insights into crop health. The application includes a comprehensive database on rice leaf diseases, aiding in education and mitigation strategies. By translating advanced research into a user-friendly mobile tool, this project revolutionizes onfield disease monitoring, fostering sustainable farming practices for increased agricultural productivity.