Plant Disease Detection Using Convolutional Neural Networks with PyTorch

This project presents an image-based deep learning solution for detecting diseases in potato leaves using Convolutional Neural Networks (CNNs). Given the prevalence and economic impact of diseases like early blight and late blight, timely and accurate detection can significantly reduce crop losses. The model is trained on a curated dataset of healthy and infected potato leaves and implemented using PyTorch. Achieving an accuracy of 97%, the system is deployed via a Flask-based web application, enabling users to perform real-time disease diagnosis. The approach integrates modern machine learning techniques withaccessible technologies, offering a scalable solution for precision agriculture.