TomatoLeafDetect

Overview

TomatoLeafDetect is a machine learning-based application for detecting diseases in tomato leaves. This project uses image classification techniques to identify various diseases affecting tomato plants, helping farmers and gardeners quickly diagnose plant health issues.

Features

• Disease Detection: Identifies various tomato leaf diseases from images
• User-friendly Interface: Easy-to-use application for uploading and analyzing leaf images
• Fast Results: Quick processing and classification of uploaded images
• High Accuracy: Trained model with high precision in disease identification

Technologies Used

• TensorFlow/Keras for model development
• Python for backend processing
• Image processing libraries (OpenCV, PIL)
• Flask for API development (if applicable)

Installation

# Clone the repository
git clone https://github.com/fathirarya/TomatoLeafDetect.git

# Navigate to the project directory
cd TomatoLeafDetect

# Install required dependencies
pip install -r requirements.txt

Usage

1. Prepare your tomato leaf image for analysis
2. Run the application:

   python app.py

3. Upload the leaf image through the interface
4. View the detection results and disease classification

Model Information

The disease detection model was trained on a comprehensive dataset of tomato leaf images with various disease conditions including:

• Early Blight
• Late Blight
• Bacterial Spot
• Target Spot
• Mosaic Virus
• Yellow Leaf Curl Virus
• Healthy leaves

The model architecture uses convolutional neural networks (CNN) optimized for plant disease detection.

Project Structure

TomatoLeafDetect/
├── app.py                 # Main application file
├── models/                # Contains trained model files
├── data/                  # Data processing scripts and utilities
├── static/                # Static files for web interface (if applicable)
├── templates/             # HTML templates (if applicable)
├── utils/                 # Utility functions
├── requirements.txt       # Required Python packages
└── README.md              # Project documentation

Contributing

Contributions to improve TomatoLeafDetect are welcome! To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes
4. Commit your changes (git commit -m 'Add some feature')
5. Push to the branch (git push origin feature-branch)
6. Create a new Pull Request

License

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

Contact

• Developer: Fathir Arya
• GitHub: fathirarya

Acknowledgements

• Plant Village Dataset for training images
• TensorFlow and Keras communities
• Contributors and supporters of the project