Plant Disease Detection System

This project was developed as part of the Smart India Hackathon. It is a web-based application that predicts plant diseases from uploaded leaf images using a deep learning model. The system is designed to assist farmers and agriculturists in identifying diseases in various crops and providing information on how to manage or treat them.

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Model Details
• Screenshots
• Contributing
• Acknowledgements
• License

Overview

Plant diseases can have a significant impact on crop yield and quality. Early detection of these diseases can help mitigate their effects. This system allows users to upload an image of a plant leaf, and it predicts the type of disease (if any) using a deep learning model trained on various plant diseases. Along with the prediction, the system provides information on how to prevent or treat the disease.

Features

• Upload images of plant leaves for disease prediction.
• Deep learning-based model for high accuracy in disease detection.
• Detailed information about each disease, including causes and preventive measures.
• Simple and intuitive user interface.
• Responsive design that works across devices.

Technologies Used

• Backend: Flask
• Frontend: React.js
• Deep Learning Model: PyTorch, ResNet9
• Image Processing: Pillow, torchvision
• Data: Custom dataset of plant leaf diseases
• Other: Flask-CORS, HTML/CSS

Installation

Prerequisites

• Python 3.8 or above
• Node.js
• pip (Python package installer)

Clone the Repository

git clone https://github.com/your-username/plant-disease-detection.git
cd plant-disease-detection

Application Setup

1. Install Python dependencies:

pip install -r backend/requirements.txt

2. Navigate to the farmwise folder:

cd farmwise

3. Install Node.js dependencies:

npm install

4. Start the React development server and flask backend server:

npm start

Usage

1. Open the application in your web browser at http://localhost:3000.
2. Select an image of a plant leaf to upload.
3. Click the "Predict" button to get the disease prediction.
4. View the result on the prediction page, which includes the disease name, cause, and prevention/treatment steps.

Model Details

The plant disease detection model is based on the ResNet9 architecture. It was trained on a dataset containing multiple classes of plant diseases, including:

• Apple scab
• Bacterial spot
• Early blight
• Powdery mildew
• Tomato Yellow Leaf Curl Virus
• And many more...

The model uses image preprocessing techniques such as resizing and normalization, followed by classification into disease categories.

Screenshots

Include screenshots here to show how the app looks and functions.

Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push the changes to your branch (git push origin feature-branch).
5. Open a pull request.

Acknowledgements

• Inspiration for this project came from the GitHub account AnMol12499.
• The frontend template was adapted from Creative-Tim.

License

This project is licensed under the MIT License. See the LICENSE file for details.