Grape Leaf Disease Detection Using Convolutional Neural Network

Abstract

This project focuses on developing a plant disease detection system using Convolutional Neural Networks (CNN) to address the critical challenge of identifying plant diseases early in agriculture. The proposed system leverages image analysis to classify diseases such as Grape Black Rot, Leaf Blight, and healthy conditions in grape leaves. Utilizing AlexNet architecture in MATLAB, the model processes a dataset of 500 leaf images (70% for training, 30% for testing) with image preprocessing techniques like resizing and normalization. The methodology involves designing a MATLAB-based GUI for user interaction, allowing image uploads, disease detection, affected area analysis, and remedy suggestions. Model performance was evaluated on multiple metrics, achieving an overall accuracy of 99.3% on the validation dataset. Tests on 60 samples consistently demonstrated high prediction confidence (96.42%-100%) and accurate classification of healthy and diseased leaves. Quantitative analysis of the affected area using clustering revealed detailed insights into disease severity, supporting effective decision-making. This system shows strong potential for real-time agricultural applications, contributing to sustainable farming practices and enhancing food security. Future enhancements include integrating mobile platforms for broader accessibility.