An end-to-end deep learning project that uses computer vision to classify chicken diseases from images. This project implements a robust CNN architecture to help farmers and veterinarians quickly identify common chicken diseases, enabling faster treatment and better flock management.
- Real-time disease classification
- Modern web interface with drag-and-drop functionality
- High-accuracy prediction model
- Confidence score for predictions
- Support for multiple image formats
- Responsive design for all devices
- TensorFlow/Keras for deep learning
- CNN Architecture for image classification
- Python for backend processing
- NumPy for numerical computations
- OpenCV for image processing
- Flask for backend server
- HTML/CSS/JavaScript for frontend
- Bootstrap for responsive design
- jQuery for AJAX requests
- Font Awesome for icons
- Docker for containerization
- DVC for data version control
- GitHub Actions for CI/CD
- AWS/Azure for cloud deployment
- Python 3.8 or higher
- pip package manager
- Virtual environment (recommended)
- Clone the repository
git clone https://github.com/ArihantSingla21/Chicken-Disease-Classification.git
cd Chicken-Disease-Classification@Project Structure
Chicken-Disease-Classification/
├── config/
│ └── config.yaml # Configuration settings
├── src/
│ └── chicken_disease_classification/
│ ├── components/ # Core components
│ │ └── data_ingestion.py
│ ├── config/ # Configuration handling
│ │ └── configuration.py
│ ├── constants/ # Project constants
│ ├── entity/ # Data classes
│ │ └── config_entity.py
│ ├── pipeline/ # Processing pipelines
│ │ └── stage_01_data_ingestion.py
│ └── utils/ # Utility functions
│ └── common.py
├── templates/ # HTML templates
│ └── index.html # Main web interface
├── static/ # Static files
├── research/ # Research notebooks
│ ├── trials.ipynb
│ └── experiments.ipynb
├── app.py # Flask application
├── main.py # Entry point
├── params.yaml # Model parameters
├── requirements.txt # Dependencies
└── setup.py # Package setup
- Create and activate virtual environment
python -m venv venv
source venv/bin/activate # For Linux/Mac
venv\Scripts\activate # For Windows- Install dependencies
pip install -r requirements.txt- Run the application
python app.pyThe application will be available at http://localhost:8080
-
Data Ingestion
- Download and extract dataset
- Split into train/test sets
- Validate data integrity
-
Data Preprocessing
- Image resizing and normalization
- Data augmentation
- Format standardization
-
Model Development
- CNN architecture design
- Model training and validation
- Hyperparameter tuning
-
Deployment
- Flask web application
- API development
- Docker containerization
- Training Accuracy: ~95%
- Validation Accuracy: ~93%
- Test Accuracy: ~92%
Supported Disease Classifications:
- Coccidiosis
- Salmonella
- New Castle Disease
- Healthy Chicken
- Access the web interface
- Upload a chicken image through drag-drop or file selection
- Click "Analyze" to get disease prediction
- View results with confidence scores
The project uses YAML configuration files:
config/config.yaml: Main configurationparams.yaml: Model parameters
Example configuration:
artifacts_root: artifacts
data_ingestion:
root_dir: artifacts/data_ingestion
source_URL: <dataset_url>
local_data_file: artifacts/data_ingestion/data.zip
unzip_dir: artifacts/data_ingestion- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit changes (
git commit -m 'Add AmazingFeature') - Push to branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Arihant Singla
- Email: arihantsingla21@gmail.com
- GitHub: @ArihantSingla21
- Dataset providers
- Open source community
- Research papers and references
For support, email arihantsingla21@gmail.com or open an issue in the repository.