Data Farm - Smart & Sustainable Agriculture Platform with AI & ODD

Revolutionizing agriculture with AI and sustainable practices

## 📋 Overview

Data Farm is an intelligent desktop application developed as part of the Esprit School of Engineering university project.

The platform aims to revolutionize agricultural management through modern technologies by integrating:

• ✅ Digital farm management system
• ✅ Intelligent automation of crops, lands, and resources
• ✅ Artificial Intelligence (YOLOv8) for plant disease and weed detection
• ✅ Sustainable Development Goals (SDG) tracking and implementation

🌱 Sustainable Development Goals (SDG)

Data Farm actively contributes to the UN's Sustainable Development Goals:

🔋 Goal 7: Affordable and Clean Energy

• Promotion of renewable energy from recycled agricultural waste (biogas)

♻️ Goal 12: Responsible Consumption and Production

• Sustainable management of agricultural resources
• Resource optimization and waste reduction
• Efficient agricultural waste management with intelligent recycling processes

🌍 Goal 13: Climate Action

• Promotion of renewable energy and circular economy solutions
• Reduction of environmental impacts through intelligent and sustainable management

⭐ Key Features

User Management

• Multi-role system (Admin, Farmer, Client, Worker)
• Authentication with secure login/signup
• Password reset functionality

Land & Crop Management

• Digital parcel creation with precise geolocation
• Real-time temperature monitoring via Weather API
• Crop selection and monitoring tailored to land specifications

Product Management

• Agricultural product listing and e-commerce functionality
• Order processing and tracking
• Sales statistics and reporting

Waste Management

• Agricultural waste declaration and tracking
• Intelligent recycling allocation
• Waste collection PDF reports

Equipment Management

• Equipment listing and availability tracking
• Reservation system for workers
• SMS notifications for reservation confirmations

Communication

• Claim/support ticket system
• Email notifications for product sales
• QR code generation for orders

AI-Powered Analysis

• YOLOv8 Disease Detection: Identifies unhealthy plants and crops
• YOLOv8 Weed Classification: Distinguishes between harmful and beneficial weeds

🛠️ Technologies

Backend & Frontend

• Java 17+
• JavaFX for modern UI
• Scene Builder for UI design
• FXML for UI structure

Database

• MySQL database
• Hibernate ORM for database mapping

AI & Image Processing

• Python 3.8+ (integrated via ProcessBuilder)
• Ultralytics YOLOv8
• DJL (Deep Java Library) for AI integration
• OpenCV for image processing

APIs & Services

• OkHttp/Retrofit for REST API calls
• Weather API (geolocation and real-time temperature)
• JavaMail API for email notifications
• Twilio API for SMS services
• ZXing for QR code generation
• iText for PDF generation

Build Tools

• Maven
• Git for version control

📥 Installation

Prerequisites

• JDK 17 or higher
• MySQL 8.0+
• Python 3.8+ (for AI features)
• Maven 3.6+

Setting up the JavaFX Application

# Clone repository
git clone https://github.com/your-username/data-farm.git
cd data-farm

# Build the project with Maven
mvn clean install

# Run the application
mvn javafx:run

Database Setup

-- Create database
CREATE DATABASE datafarm_db;

-- Create user and grant privileges
CREATE USER 'datafarm_user'@'localhost' IDENTIFIED BY 'password';
GRANT ALL PRIVILEGES ON datafarm_db.* TO 'datafarm_user'@'localhost';
FLUSH PRIVILEGES;

Setting up Python & YOLOv8 for AI Features

# Install Python 3.8+ from https://www.python.org/downloads/

# Create and activate virtual environment
python -m venv venv

# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

# Update pip
pip install --upgrade pip

# Install Ultralytics (YOLOv8)
pip install ultralytics

# Install other dependencies
pip install opencv-python numpy pillow

# Verify installation
yolo

🚀 Usage

For Farmers

1. Create your account and set up your farm profile
2. Add and manage your land parcels with geolocation
3. Select appropriate crops based on land characteristics
4. Monitor real-time weather conditions
5. List agricultural products for sale
6. Manage equipment and waste recycling

For Clients

1. Browse available agricultural products
2. Place orders with secure checkout
3. Track order status

For Workers

1. View available equipment
2. Make reservations for needed tools
3. Receive SMS confirmations

For Administrators

1. Manage user accounts and permissions
2. Handle support tickets and claims
3. Access comprehensive statistics and reports

🔄 Development Workflow

Project Structure

data-farm/
├── src/
│   ├── main/
│   │   ├── java/
│   │   │   └── edy/connection/
│   │   │       ├── app/        # Main application
│   │   │       ├── controller/ # JavaFX controllers
│   │   │       ├── model/      # Data models
│   │   │       ├── dao/        # Data access objects
│   │   │       ├── service/    # Business logic
│   │   │       ├── util/       # Utility classes
│   │   │       └── ai/         # AI integration
│   │   ├── resources/
│   │   │   ├── fxml/           # UI layout files
│   │   │   ├── css/            # Stylesheets
│   │   │   ├── images/         # Images and icons
│   │   │   └── python/         # Python scripts for AI
│   │   └── META-INF/
│   └── test/                   # Unit tests
├── pom.xml                     # Maven configuration
└── README.md

Running the Application

# Start the application with Maven
mvn javafx:run

# Run AI services separately if needed
cd src/main/resources/python
python run_detection.py

Testing

# Run tests with Maven
mvn test

🤝 Contributing

We welcome contributions to the Data Farm project!

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

📄 License

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

👏 Acknowledgments

• Esprit School of Engineering for project guidance
• YOLOv8 and Ultralytics for AI vision capabilities
• All contributors who have helped shape this project

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Developed with ❤️ by the Data Farm Team