🌾 KrishiSetu - Agricultural AI Advisor

A comprehensive AI-powered agricultural advisory system designed specifically for Indian farmers. This system uses multi-agent architecture with CrewAI, MCP (Model Context Protocol), and FastAPI to provide intelligent, contextual advice for agricultural decisions.

🎯 Problem Statement

KrishiSetu addresses the critical challenges faced by Indian farmers by providing AI-driven solutions to answer questions like:

• "When should I irrigate?" - Weather-based irrigation recommendations
• "What seed variety suits this unpredictable weather?" - Climate-adaptive crop selection
• "Will next week's temperature drop kill my yield?" - Weather risk assessment
• "Can I afford to wait for the market to improve?" - Market trend analysis
• "Where can I get affordable credit, and will any state/central government policy help me with finances?" - Financial guidance and policy information

🏗️ Architecture

Multi-Agent System

The system uses three specialized AI agents working together:

1. 🌤️ Weather Agent - Provides weather analysis and irrigation recommendations
2. 🌱 Crop Agent - Offers crop selection and management advice
3. 💰 Finance Agent - Delivers financial guidance and government scheme information

Technology Stack

• CrewAI - Multi-agent orchestration and task management
• MCP (Model Context Protocol) - External data integration
• FastAPI - High-performance web API
• LangChain - LLM integration and tool management
• SQLAlchemy - Database management
• Pydantic - Data validation and serialization

🚀 Features

🌍 Multilingual Support

• English, Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Kannada, Malayalam, Punjabi
• Automatic language detection
• Localized responses for Indian farmers

📊 Real-time Data Integration

• Weather data from OpenWeatherMap API
• Government agricultural data
• Market price information
• Soil health data

🎯 Intelligent Query Processing

• Natural language understanding
• Context-aware responses
• Confidence scoring
• Multi-domain synthesis

🔧 Extensible Architecture

• Modular agent system
• Plugin-based data sources
• Configurable workflows
• Easy integration with external systems

📦 Installation

Prerequisites

• Python 3.8+
• pip package manager

Setup Instructions

1. Clone the repository

   git clone <repository-url>
   cd KrishiSetu

2. Install dependencies

   uv pip install -r requirements.txt

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your API keys

4. Initialize the database

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

5. Run the application

   python main.py

🔑 Configuration

Required API Keys

• OpenAI API Key - For LLM-powered agents
• Tavily API Key - For web search capabilities
• Weather API Key - For weather data (OpenWeatherMap)

Environment Variables

OPENAI_API_KEY=your_openai_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here
WEATHER_API_KEY=your_weather_api_key_here
DATABASE_URL=sqlite:///./krishisetu.db
DEBUG=True
HOST=0.0.0.0
PORT=8000

🎮 Usage

API Endpoints

1. Process Agricultural Query

POST /query
Content-Type: application/json

{
  "query": "When should I irrigate my wheat crop?",
  "context": {
    "location": "Mumbai",
    "crop_type": "Wheat",
    "farmer_type": "small"
  },
  "comprehensive": true,
  "language": "en"
}

2. Weather-Specific Query

POST /query/weather
Content-Type: application/json

{
  "query": "क्या आज बारिश होगी?",
  "context": {"location": "Delhi"}
}

3. Crop-Specific Query

POST /query/crop
Content-Type: application/json

{
  "query": "Which crop should I plant this season?",
  "context": {
    "location": "Punjab",
    "soil_type": "Alluvial",
    "season": "Kharif"
  }
}

4. Finance-Specific Query

POST /query/finance
Content-Type: application/json

{
  "query": "What loans are available for farmers?",
  "context": {
    "farmer_type": "small",
    "land_area": 2.0,
    "state": "Maharashtra"
  }
}

Example Responses

Weather Query Response

{
  "success": true,
  "data": {
    "current_weather": {
      "temperature": 28.5,
      "humidity": 65,
      "description": "partly cloudy"
    },
    "irrigation_recommendation": {
      "recommendation": "Moderate irrigation recommended",
      "priority": "Medium",
      "next_irrigation": "Within 48 hours"
    }
  },
  "confidence": 0.85,
  "source": "Weather Agent"
}

Crop Query Response

{
  "success": true,
  "data": {
    "recommendations": [
      {
        "name": "Rice",
        "varieties": ["IR64", "Swarna"],
        "suitability_score": 85,
        "market_price": 1800
      }
    ],
    "crop_calendar": {
      "planting_start": "June",
      "harvest_start": "September"
    }
  },
  "confidence": 0.78,
  "source": "Crop Agent"
}

🧪 Testing

Run the test suite to ensure all components work correctly:

python -m pytest tests/

Or run specific test categories:

# Test agents
python tests/test_agents.py

# Test language processing
python -c "from tests.test_agents import TestLanguageProcessor; unittest.main()"

📁 Project Structure

KrishiSetu/
├── agents/                 # AI agent implementations
│   ├── base_agent.py      # Base agent class
│   ├── weather_agent.py   # Weather analysis agent
│   ├── crop_agent.py      # Crop management agent
│   └── finance_agent.py   # Financial advisory agent
├── api/                   # FastAPI application
│   └── main.py           # Main API endpoints
├── crew/                  # CrewAI orchestration
│   └── agricultural_crew.py
├── mcp/                   # Model Context Protocol
│   └── mcp_client.py     # MCP client implementation
├── models/                # Database models
│   └── database.py       # SQLAlchemy models
├── utils/                 # Utility functions
│   └── language_processor.py
├── tests/                 # Test suite
│   └── test_agents.py
├── config.py             # Configuration management
├── main.py               # Application entry point
├── requirements.txt      # Python dependencies
└── README.md            # This file

🔧 Development

Adding New Agents

1. Create a new agent class inheriting from BaseAgent

2. Implement required methods:

   • _get_backstory()
   • _get_tools()
   • process_query()
   • _get_keywords()

3. Register the agent in the crew:

from crew.agricultural_crew import AgriculturalCrew

class NewAgent(BaseAgent):
    def __init__(self):
        super().__init__(
            name="New Expert",
            role="New Specialist",
            goal="Provide new type of advice"
        )
    
    # Implement required methods...

Adding New Data Sources

1. Extend the MCP client in mcp/mcp_client.py
2. Add new data retrieval methods
3. Update the data provider in crew/agricultural_crew.py

Customizing Language Support

1. Add new language patterns in utils/language_processor.py
2. Update the language_patterns dictionary
3. Add translation mappings

🌟 Key Innovations

1. Multi-Agent Collaboration

• Agents work together using CrewAI for comprehensive analysis
• Each agent specializes in specific domains
• Coordinated responses provide holistic advice

2. Context-Aware Processing

• Extracts location, crop type, and farmer context
• Provides personalized recommendations
• Considers local conditions and constraints

3. Multilingual Intelligence

• Automatic language detection
• Localized agricultural terminology
• Cultural context awareness

4. Real-time Data Integration

• Weather data integration
• Market price monitoring
• Government scheme updates

5. Explainable AI

• Confidence scoring for responses
• Source attribution
• Reasoning transparency

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

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

📄 License

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

🙏 Acknowledgments

• CrewAI - For multi-agent orchestration
• LangChain - For LLM integration
• FastAPI - For high-performance API framework
• Indian Agricultural Research Institute - For agricultural data
• OpenWeatherMap - For weather data
• Government of India - For agricultural policies and schemes

📞 Support

For support and questions:

• Create an issue on GitHub
• Contact the development team
• Check the documentation

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KrishiSetu - Bridging the gap between technology and agriculture for a sustainable future! 🌾🤖