Agriculture Drone Ground Control Station

A comprehensive web-based ground control station for agricultural drone operations, featuring real-time telemetry, mission planning, AI-powered anomaly detection, and field management.

🚀 DEPLOYED & LIVE!

🌐 Backend API: https://agri-7.onrender.com
📖 API Documentation: https://agri-7.onrender.com/docs
✅ Health Check: https://agri-7.onrender.com/health

Quick Start for Team Members

No backend setup needed! The backend is already deployed and running.

1. Clone the repository:

   git clone https://github.com/Advaithftw/agri.git
   cd agri

2. Setup frontend only:

   cd frontend
   npm install
   npm start

3. Access the app:

   • Frontend: http://localhost:3000
   • Backend: https://agri-7.onrender.com (already running)

Full Local Development Setup (Optional)

If you want to run everything locally:

Backend Setup

1. Navigate to backend directory:

   cd backend

2. Create virtual environment:

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Linux/Mac
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure database:

   # Copy environment template
   cp .env.example .env
   # Edit .env and set your DATABASE_URL (see docs/supabase-setup.md for details)

5. Initialize database:

   python -c "from database import create_tables; create_tables()"

6. Start the server:

   python main.py

Local backend will be available at http://localhost:8000

Frontend Setup

1. Navigate to frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. For local backend, create .env.local:

   REACT_APP_API_BASE_URL=http://localhost:8000

4. Start development server:

   npm start

Frontend will be available at http://localhost:3000

Simulator Setup

1. Navigate to simulator directory:

   cd simulator

2. Install dependencies:

   pip install asyncio websockets numpy

3. Start simulator:

   python simulator.py

📋 Features

✅ Core Features

• User Authentication - JWT-based secure login/registration
• Field Management - Create, edit, and visualize agricultural fields
• Mission Planning - Design drone missions with waypoints and actions
• Real-time Telemetry - Live drone status, GPS, battery, and sensor data
• AI Anomaly Detection - Machine learning-powered alert system
• Data Export - Export telemetry and mission data (CSV, JSON)
• Interactive Maps - Leaflet-based field and mission visualization
• WebSocket Communication - Real-time updates and alerts

🔧 Technical Features

• FastAPI Backend - Modern, fast web API with automatic documentation
• React Frontend - Responsive TypeScript interface with Material-UI
• SQLAlchemy ORM - Robust database management with SQLite
• Realistic Simulator - Physics-based drone flight simulation
• Comprehensive Testing - Unit, integration, and E2E test suites
• Documentation - Complete API docs and deployment guides

Architecture

├── backend/          # FastAPI REST API + WebSocket server
├── frontend/         # React web application
├── simulator/        # Drone flight simulator service  
└── docs/            # Architecture and API documentation

Tech Stack

• Backend: FastAPI, SQLAlchemy, PostgreSQL (Supabase), WebSockets, JWT Authentication
• Frontend: React, Leaflet maps, Material-UI, WebSocket client
• Simulator: Python asyncio service with realistic flight physics
• AI/ML: scikit-learn (Isolation Forest), numpy, pandas
• Database: PostgreSQL via Supabase (cloud-hosted, multi-user support)

Quick Start

Prerequisites

• Python 3.9+
• Node.js 16+
• npm or yarn

Backend Setup

cd backend
python -m venv venv
# Windows
venv\Scripts\activate
# Linux/Mac  
source venv/bin/activate

pip install -r requirements.txt
python -m uvicorn main:app --reload --port 8000

Frontend Setup

cd frontend
npm install
npm start

Simulator Setup

cd simulator
python -m venv venv
# Windows
venv\Scripts\activate
# Linux/Mac
source venv/bin/activate

pip install -r requirements.txt
python simulator.py

Access the Application

• Frontend: http://localhost:3000
• Backend API (Deployed): https://agri-7.onrender.com
• API Documentation: https://agri-7.onrender.com/docs
• Local Backend (if running): http://localhost:8000

Database Setup

This application uses Supabase (PostgreSQL) for cloud-hosted, multi-user database support.

For detailed setup instructions, see: docs/supabase-setup.md

Quick setup:

1. Create a free Supabase project at supabase.com
2. Copy your connection string to backend/.env
3. Run database initialization: python -c "from database import create_tables; create_tables()"

API Endpoints

Authentication

• POST /auth/register - User registration
• POST /auth/login - User login
• GET /auth/me - Get current user profile

Field Management

• GET /fields - List user fields
• POST /fields - Create new field
• GET /fields/{id} - Get field details
• PUT /fields/{id} - Update field
• DELETE /fields/{id} - Delete field

Mission Management

• GET /missions - List user missions
• POST /missions - Create new mission
• GET /missions/{id} - Get mission details
• PUT /missions/{id} - Update mission
• DELETE /missions/{id} - Delete mission
• POST /missions/{id}/start - Start mission execution
• POST /missions/{id}/pause - Pause mission
• POST /missions/{id}/resume - Resume mission
• POST /missions/{id}/abort - Abort mission

Telemetry & Logs

• GET /telemetry/{mission_id} - Get mission telemetry history
• GET /logs - List flight logs with filters
• GET /logs/{id}/export - Export log as CSV/JSON

AI Analytics

• GET /ai/anomalies/{mission_id} - Get anomaly detection results
• POST /ai/predict-battery - Battery drain prediction

WebSocket

• wss://agri-7.onrender.com/ws/telemetry/{mission_id} - Real-time telemetry stream (deployed)
• ws://localhost:8000/ws/telemetry/{mission_id} - Real-time telemetry stream (local)

Database Schema

See docs/database-schema.md for detailed table structures.

Development

Running Tests

# Backend tests
cd backend
pytest

# Frontend tests  
cd frontend
npm test

Code Formatting

# Backend
cd backend
black . && isort .

# Frontend
cd frontend  
npm run format

Deployment

See docs/deployment.md for production deployment instructions.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Acknowledgments

• Inspired by QGroundControl (QGCS) for UI/UX patterns
• Uses open-source mapping libraries and ML models
• Built for agricultural technology advancement