Potato diseases can significantly affect crop health and reduce agricultural productivity if they are not identified at an early stage. This project is an AI-powered potato disease detection system that uses Deep Learning and Computer Vision to identify diseases from images of potato leaves.

The application allows users to upload an image of a potato leaf, and the trained Convolutional Neural Network (CNN) model analyzes the image and predicts the disease category. This project demonstrates how Artificial Intelligence can be used to assist in agricultural monitoring and disease identification.

Problem Statement

Early detection of potato leaf diseases is important for maintaining healthy crops and reducing losses. Manual identification can be time-consuming and may require expert knowledge. This project aims to develop an automated system that classifies potato leaf diseases from images using Deep Learning techniques.

Features

Image upload for potato leaf analysis.

AI-powered disease classification.

CNN-based image processing.

Simple and user-friendly web interface.

Fast prediction of disease category.

Technology Stack

Programming Language

Python

Libraries and Frameworks

TensorFlow

FastAPI

NumPy

OpenCV

Deep Learning Model

Convolutional Neural Network (CNN)

Dataset

The model was trained using a publicly available potato leaf disease dataset. Before training, the images were preprocessed by resizing and normalizing them to prepare the data for model training.

Project Workflow

Collect the potato leaf disease dataset.

Preprocess the images.

Train the CNN model.

Save the trained model.

Develop the web application using FastAPI.

Upload a potato leaf image.

Process the uploaded image.

Predict the disease category.

Display the prediction to the user.

Challenges Faced

One of the main challenges was obtaining consistent predictions for images captured under different lighting conditions and backgrounds. I improved the preprocessing pipeline and tested the application with different sample images to achieve more reliable predictions.

What I Learned

Through this project, I gained practical experience in Deep Learning, Computer Vision, image preprocessing, TensorFlow, FastAPI, model integration, and building AI-powered web applications.

Future Improvements

Improve the model by training with a larger and more diverse dataset.

Support detection of additional plant diseases.

Deploy the application on a cloud platform.

Develop a mobile-friendly version.

Provide disease information and preventive recommendations along with the prediction.

Conclusion

This project demonstrates how Artificial Intelligence and Deep Learning can be applied to assist in identifying potato leaf diseases from images. It strengthened my understanding of Computer Vision, Convolutional Neural Networks, TensorFlow, FastAPI, image preprocessing, and the process of developing and deploying an AI-based application.