A Novel Disease Diagnosis Framework for Tomato Plant Leaves Disease Detection

Abstract

"This project proposes a novel disease diagnosis ensemble model for detecting tomato plant leaf diseases using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to accurately 9 types of diseases affecting tomato leaves with a high level of accuracy. The proposed ensemble model has shown an accuracy of 96.5% on the PlantsVillage data set from Kaggle. The ensemble model aims to address the challenges faced bye farmers in accurately and timely identifying various tomato leaf diseases. By leveraging deep learning techniques and image processing, the project seeks to develop a user-friendly web application that enables farmers to upload images of tomato leaves and receive automated disease diagnoses. The proposed framework will utilize a CNN model and ViT model trained on a diverse dataset of tomato leaf images, incorporating data augmentation techniques to enhance accuracy and robustness. The web application will provide farmers with real-time disease identification, facilitating early intervention and effective disease management strategies. "