Banana Leaf Disease Detection using ML & Computer Vision Algorithm

Abstract

Banana cultivation is highly susceptible to various leaf diseases, which significantly reduce crop yield and quality. This project presents a web-based application for automatic banana leaf disease detection using Machine Learning (ML) and Computer Vision algorithms. The system allows users to load and process banana leaf image datasets, train detection models, and predict diseases through a user-friendly interface. Upon image upload, the trained model classifies the leaf condition and provides a disease prediction with an associated accuracy score. As demonstrated in the application, diseases like Black Sigatoka can be detected with notable precision. The project integrates efficient image processing techniques and machine learning classifiers, enabling early and accurate diagnosis to assist farmers and agronomists in disease management.