Deep Learning based Plant Disease Diagnosis and Remedy Recommendation System for Agricultural Sustainability

This project proposes a solution to address the challenges faced by farmers in identifying plant diseases at an early stage, which can affect the quantity and quality of their yield. The solution leverages Deep Learning and Convolutional Neural Networks (CNN) to efficiently identify various plant diseases. The proposed solution involves training a CNN model using a dataset consisting of both affected and unaffected plant leaf samples, collected from benchmark repositories. Once the model is trained, it can be deployed as a web application that farmers can access via their website application. The application will enable farmers to upload images of their plant leaves and receive a diagnosis of the disease, along with recommendations on the appropriate treatment or prevention measures based on the identified disease. The web application will also contain an interface for agricultural experts to review the model’s results and provide additional solutions through the comment section. By developing a web application that integrates agricultural experts’ knowledge, the proposed solution aims to create a robust and effective tool that helps mitigate the impact of plant diseases and ensures food security for future generations. Key words- Deep Learning, Convolution Neural Network (CNN