Plowtion harnesses the power of AI to generate personalized crop schedules tailored to your specific location and crop type. By integrating real-time weather data, we help farmers make informed decisions that optimize growth and maximize yield, adapting to the challenges posed by changing climate conditions.

• 🤖 AI-Powered Crop Recommendations: Machine learning model provides personalized crop suggestions based on environmental factors
• 📅 Smart Scheduling: Generate optimized planting and harvesting schedules
• 🌤️ Weather Integration: Real-time weather data integration for informed decision-making
• 🛒 Marketplace: Robust platform for buying and selling seeds and farming equipment
• 💬 AI Chatbot: NLP-powered assistant providing instant farming tips and guidance
• 👤 User Profiles: Personalized farmer profiles and preferences
• 🔐 Secure Authentication: OAuth integration with Google authentication

Plowtion/
├── backend/                 # Python Flask API
│   ├── app.py              # Main Flask application
│   ├── dataset/            # ML training datasets
│   ├── requirements.txt    # Python dependencies
│   └── Dockerfile         # Backend container config
├── frontend/               # Next.js React application
│   ├── app/               # Next.js app directory
│   ├── components/        # Reusable React components
│   ├── lib/              # Utility libraries
│   ├── services/         # API service layer
│   └── Dockerfile        # Frontend container config
├── model/                 # Machine learning models
│   └── crop_model.joblib # Trained crop recommendation model
└── docker-compose.yml    # Multi-container orchestration

• Docker & Docker Compose (recommended)
• Python 3.8+ (for local development)
• Node.js 18+ (for local development)
• MongoDB (included in Docker setup)

1. Clone the repository

   git clone <repository-url>
   cd Plowtion

2. Configure environment variables

   cp .env.example .env
   # Edit .env with your actual API keys and secrets

3. Start the application

   docker-compose up -d

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5000
   • MongoDB: localhost:27017

1. Navigate to backend directory

   cd backend

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Set environment variables

   export FLASK_ENV=development
   export PERPLEXITY_API_KEY=your_api_key_here
   # ... other environment variables from .env.example

5. Run the Flask application

   python app.py

1. Navigate to frontend directory

   cd frontend

2. Install dependencies

   npm install

3. Set environment variables

   cp .env.example .env.local
   # Edit .env.local with your values

4. Run the development server

   npm run dev

• PERPLEXITY_API_KEY: For AI chatbot functionality
• WEATHER_API_KEY: For weather data integration
• GOOGLE_CLIENT_ID & GOOGLE_CLIENT_SECRET: For OAuth authentication
• NEXTAUTH_SECRET: For NextAuth.js session encryption
• AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY: For AWS services

Create a .env file based on .env.example and fill in your actual values:

# API Keys
PERPLEXITY_API_KEY=your_perplexity_api_key
WEATHER_API_KEY=your_weather_api_key
GOOGLE_CLIENT_ID=your_google_client_id
GOOGLE_CLIENT_SECRET=your_google_client_secret

# Security
NEXTAUTH_SECRET=your_secure_random_string
NEXTAUTH_URL=http://localhost:3000

# Database
MONGODB_URI=mongodb://localhost:27017/plowtion

# AWS (if using AWS services)
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-1

• Development: http://localhost:5000
• Production: https://your-domain.com/api

// Get crop recommendation
const response = await fetch('/api/predict', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    nitrogen: 40,
    phosphorus: 50,
    potassium: 30,
    temperature: 25,
    humidity: 80,
    ph: 6.5,
    rainfall: 200
  })
});

cd backend
python -m pytest tests/

cd frontend
npm test

1. Build production images

   docker-compose -f docker-compose.prod.yml build

2. Deploy with production configuration

   docker-compose -f docker-compose.prod.yml up -d

The application is containerized and can be deployed on:

• AWS ECS/EKS
• Google Cloud Run
• Azure Container Instances
• DigitalOcean App Platform

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

• Follow PEP 8 for Python code
• Use ESLint and Prettier for JavaScript/TypeScript
• Write tests for new features
• Update documentation for API changes
• Ensure Docker builds pass

• No hardcoded secrets: All sensitive data is managed through environment variables
• Authentication: Secure OAuth integration
• API Security: Rate limiting and input validation
• Data Protection: Encrypted data transmission

• Mobile application (React Native)
• Advanced weather predictions
• IoT sensor integration
• Multi-language support
• Advanced analytics dashboard
• Integration with farming equipment APIs

Docker containers not starting

• Check Docker daemon is running
• Verify environment variables are set
• Check port conflicts (3000, 5000, 27017)

API authentication errors

• Verify Google OAuth credentials
• Check NEXTAUTH_SECRET is set
• Ensure NEXTAUTH_URL matches your domain

Database connection issues

• Verify MongoDB is running
• Check MONGODB_URI format
• Ensure network connectivity between containers

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

• Scikit-learn for machine learning capabilities
• Next.js for the robust frontend framework
• Flask for the lightweight backend API
• Perplexity AI for intelligent chatbot functionality
• MongoDB for flexible data storage

For support and questions:

• Create an issue on GitHub
• Join our community discussions
• Check the documentation wiki

Built with ❤️ for farmers around the world 🌾