Leaf Disease Detection Using Convolutional Neural Network

Abstract

Abstract: The aim of the project is to develop a Leaf disease detection system using deep learning, specifically Convolutional Neural Networks (CNNs). The project focuses on classifying images of plant leaves into 39 different disease categories by utilizing deep learning, specifically Convolutional Neural Networks (CNNs), for plant disease detection based on leaf images. The dataset consists of 39 classes with a totalof 61,486 images, and various augmentation techniques are applied to increase dataset size. The implementation involves PyTorch, with transformations for data augmentation, dataset creation usingImage Folder, and a split into training, validation, and testing sets. The CNN model is designed for image classification, using ReLU activation and soft max for the final layer. The training process involves batch gradient descent, and the model achieves an accuracy of 87% on training data, 84% on validation data, and 83% on test data. The key objectives include utilizing image data, implementing data augmentation techniques, creating a dataset, and training a CNN model to accurately predict and classify plant diseases based on input images. The ultimate goal is to provide a tool that can assist in early detection and diagnosis of plant diseases through automated analysis of leaf images