Potato-leaf-Disease-Classification-WebApp-Complete-Project-End-to-End

This repository contains a complete project focused on classifying potato leaf diseases using machine learning techniques. The project also includes the development of a web application that allows users to classify potato leaf images for different diseases interactively.

video

Potato.leaf.Disease.Classification.mp4

Project Overview

Potato crops are susceptible to various diseases that can impact yield and quality. This project aims to provide an automated solution for identifying these diseases through image classification. The classification model is trained on a dataset of potato leaf images representing healthy leaves and various disease conditions.

Features

Preprocessing: The project includes preprocessing steps such as image resizing, normalization, and data augmentation to enhance the model's performance.
Machine Learning Model: The image classification model is built using a convolutional neural network (CNN) architecture. It's trained on labeled images to accurately classify different diseases.
Web Application: The web app allows users to upload potato leaf images and receive predictions about the presence of diseases. It provides a user-friendly interface for real-time classification.

Technologies Used

Python: Used for data preprocessing, model training, and web app development.
TensorFlow and Keras: Used to create, train, and evaluate the deep learning model.
Flask: Used to develop the web application and handle user interactions.
Next.js: Used for designing the web app's user interface.

Buy the Project

Interested in utilizing this project for your own purposes? Contact us to buy the project package, which includes:

Complete source code and pre-trained model.
In-depth documentation with step-by-step instructions.
Technical support for setup and customization.

Contact Information: