AIBASED TOMATO CROP DISEASE DIAGNOSTIC SYSTEM

Project Code :TCMAPY2208

Objective

The objective of the project is to develop an AI-based system for accurate and real-time detection of tomato crop diseases using deep learning models, providing actionable insights to farmers for better crop management.

Abstract

This project presents an AI-based Tomato Crop Disease Diagnostic System that leverages deep learning models to detect various diseases in tomato plants. By using advanced convolutional neural networks (CNNs) and pretrained models such as MobileNet v3, CNN, and ResNet, the system efficiently classifies images of tomato plants into different disease categories. The system aims to provide an automated solution to assist farmers and agriculture experts in identifying diseases like Tomato Mosaic Virus, Target Spot, Bacterial Spot, Late Blight, and more. The system provides accurate and reliable predictions with a user-friendly interface, enabling users to upload images and view the results instantly. The web application integrates Flask for the backend and MySQL for storing prediction histories, ensuring a seamless experience for users.

Keywords: AI, Tomato crop, disease detection, deep learning, MobileNet, CNN, ResNet, classification, MySQL, Flask.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.