Rayyan - AI-Powered Precision Agriculture Platform

Rayyan is an intelligent agricultural advisory system that combines IoT sensors, machine learning, and AI agents to provide data-driven recommendations for irrigation, fertigation, pest management, and crop health monitoring.

🌟 Features

• 🤖 AI Agricultural Advisor: LangGraph-based conversational agent with specialized agricultural tools
• 💧 Smart Irrigation: Automated irrigation scheduling based on soil moisture, weather, and crop needs
• 🧪 Fertigation Optimization: NPK ratio recommendations tailored to crop type and growth stage
• 🐛 Pest Detection: Early pest activity detection with confidence scoring
• 🌱 Soil & Water Analysis: Real-time monitoring of soil conditions and water quality
• 🌤️ Weather Integration: Forecast-aware decision making for farm operations
• 📊 Real-time Dashboard: Next.js frontend with live sensor data and agent chat
• 🔌 Edge Computing: Lightweight ONNX/TFLite inference on IoT devices

🏗️ Architecture

├── backend/          # FastAPI API server with LangGraph agent
├── frontend/         # Next.js 15 + React 19 web application
├── edge/             # Edge computing module for IoT devices
├── ml/               # Machine learning models and training
└── infra/            # Infrastructure and deployment configs

Tech Stack

• Backend: FastAPI, SQLAlchemy, Supabase (PostgreSQL), LangChain/LangGraph
• Frontend: Next.js 15.5, React 19, Tailwind CSS v4, TypeScript
• AI/LLM: OpenAI, Anthropic, LangGraph agents
• Database: Supabase (PostgreSQL with real-time, auth, storage)
• Edge: Python, ONNX Runtime, TFLite, MQTT
• ML: PyTorch, TensorFlow, scikit-learn

🚀 Quick Start

Using Docker (Recommended)

# 1. Clone the repository
git clone https://github.com/a-belard/rayyan.git
cd rayyan

# 2. Set up environment variables
cp backend/.env.example backend/.env
cp frontend/.env.local.example frontend/.env.local
# Edit .env files with your Supabase and API keys

# 3. Start all services
docker-compose up -d

# 4. Access the application
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docs

Using Individual Docker Compose Files

Each folder has its own docker-compose.yml for independent deployment:

# Backend only
cd backend && docker-compose up -d

# Frontend only
cd frontend && docker-compose up -d

# Edge only
cd edge && docker-compose up -d

# ML environment only
cd ml && docker-compose up -d

See DOCKER_COMPOSE_INDIVIDUAL.md for detailed guide.

Manual Setup

Backend

cd backend
cp .env.example .env
# Edit .env with your credentials
uv pip install -e .
python main.py

Frontend

cd frontend
cp .env.local.example .env.local
pnpm install
pnpm dev

Edge Computing

cd edge
pip install -r requirements.txt
python main.py

ML Training

cd ml
pip install -r requirements.txt
jupyter lab

See detailed setup instructions in SETUP_SUPABASE.md and DOCKER.md.

📖 Documentation

Main Guides

• SETUP_SUPABASE.md - Supabase backend setup guide
• DOCKER.md - Complete Docker deployment guide
• DOCKER_COMPOSE_INDIVIDUAL.md - Individual service deployment
• AGENT_IMPLEMENTATION.md - AI agent architecture
• AGENT_SUMMARY.md - Quick reference for agent system
• CONTRIBUTING.md - Contribution guidelines

Additional Documentation

• docs/docker-compose-individual.md - Detailed individual service guide
• docs/docker-compose-quick-reference.md - Quick command reference
• docs/ - Additional technical documentation

🤖 AI Agent Usage

The Rayyan AgriAdvisor provides expert agricultural guidance through natural conversation:

# Example queries:
"Check soil conditions in Zone 1"
"What's the weather forecast for the next 3 days?"
"Calculate irrigation schedule for tomatoes in vegetative stage"
"Recommend fertigation for corn in flowering stage"
"Are there any pest threats detected?"
"Analyze water quality from Well 2"

Available Tools

1. Weather Forecast - Rainfall, temperature, evapotranspiration
2. Soil Analysis - Moisture %, EC (salinity), pH, nutrients
3. Water Quality - EC, pH, RO blending recommendations
4. Pest Detection - Species identification, severity, confidence
5. Irrigation Scheduling - Timing, duration, volume calculations
6. Fertigation Recommendations - NPK ratios, mixing instructions

🐳 Docker Deployment

Build Individual Services

# Backend
docker build -t rayyan-backend ./backend

# Frontend
docker build -t rayyan-frontend ./frontend

# Edge
docker build -t rayyan-edge ./edge

# ML
docker build -t rayyan-ml ./ml

Using Docker Compose

# Production stack (backend + frontend + edge)
docker-compose up -d

# With ML environment for training
docker-compose --profile ml up -d

# View logs
docker-compose logs -f backend

# Stop all services
docker-compose down

See DOCKER.md for comprehensive Docker guide.

🔧 Development

Backend Development

cd backend
uv pip install -e .
python main.py  # Runs with auto-reload

Frontend Development

cd frontend
pnpm install
pnpm dev  # Uses Turbopack for fast HMR

Code Formatting

# Backend (Python)
black backend/ --line-length=88
isort backend/ --profile=black

# Frontend (TypeScript)
cd frontend
pnpm lint

🧪 Testing

# Backend tests
cd backend
pytest

# Frontend tests
cd frontend
pnpm test

📊 ML Models

The platform includes modules for:

• Weather Forecasting: Time-series prediction models
• Soil-Water Modeling: Moisture dynamics and irrigation needs
• Pest Detection: Computer vision models (YOLOv8/CNNs)
• NLU: Natural language understanding for agent queries
• Fertigation Optimization: NPK recommendation engine

Models are exported as ONNX/TFLite for edge deployment.

🌐 API Endpoints

Threads (Conversations)

• GET /threads - List all conversation threads
• POST /threads - Create new thread
• GET /threads/{id} - Get thread details
• PATCH /threads/{id} - Update thread
• DELETE /threads/{id} - Delete thread

Agent

• POST /agent/threads/{id}/run - Execute agent (SSE streaming)

See full API documentation at http://localhost:8000/docs when running.

🔐 Environment Variables

Backend (.env)

DATABASE_URL=postgresql+asyncpg://...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=your-anon-key
OPENAI_API_KEY=sk-xxx
ANTHROPIC_API_KEY=sk-ant-xxx
DEFAULT_LLM_PROVIDER=openai
DEFAULT_LLM_MODEL=gpt-4o-mini
LLM_TEMPERATURE=0.7
CHAT_HISTORY_MAX_MESSAGES=50

Frontend (.env.local)

NEXT_PUBLIC_API_URL=http://localhost:8000

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add 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

• LangChain/LangGraph for agent framework
• Supabase for backend infrastructure
• Next.js and Vercel for frontend framework
• OpenAI and Anthropic for LLM APIs

📧 Contact

For questions or support, please open an issue on GitHub.

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