AI-Powered Agricultural Robot

An advanced agricultural monitoring and automation system that combines drone surveillance, machine learning, and soil analysis for precision farming.

Features

• Drone Surveillance System

  • Automated field monitoring
  • Real-time image capture and processing
  • Configurable flight patterns and monitoring schedules
  • Battery management and automated return-to-home

• Plant Disease Detection

  • ML-powered disease classification
  • Real-time image analysis
  • Support for multiple crop diseases
  • Confidence-based alerting system

• Soil Health Analysis

  • Real-time soil parameter monitoring
  • Analysis of moisture, pH, NPK levels
  • Automated recommendations
  • Historical data tracking

• Real-time Monitoring and Alerts

  • Mobile app integration
  • Email notifications
  • Critical condition alerts
  • Performance monitoring

Technology Stack

• Python 3.13.2
• OpenCV for image processing
• Machine Learning frameworks for disease detection
• YAML for configuration management
• Firebase for data storage and real-time updates

Project Structure

.
├── config/
│   └── config.yaml         # Application configuration
├── data/
│   ├── drone_images/      # Captured drone images
│   ├── processed_images/  # Analyzed images
│   └── soil_readings/     # Soil sensor data
├── models/                # ML model files
├── src/
│   ├── drone_controller.py
│   ├── plant_disease_classifier.py
│   ├── soil_analyzer.py
│   └── main.py
└── utils/
    └── image_processing.py

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/AI-Powered-Agricultural-Robot.git
   cd AI-Powered-Agricultural-Robot

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure the application:

   • Copy config/config.yaml.example to config/config.yaml
   • Update configuration values as needed

Usage

1. Start the main application:

   python src/main.py

2. Access the monitoring dashboard:

   http://localhost:8000/dashboard

Development

• Follow PEP 8 style guide
• Write unit tests for new features
• Update documentation as needed
• Use meaningful commit messages

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - see LICENSE file for details

Support

For support, please open an issue in the GitHub repository or contact the maintainers.