AgriAI - AI-Powered Agricultural Advisor

Overview

AgriAI is a comprehensive AI-powered agricultural advisor designed to help farmers and agricultural stakeholders make informed decisions. The system addresses real-world challenges including low digital access, multilingual support, and the need for reliable, explainable AI solutions.

Key Features

🌱 Multi-Modal Query Support

• Natural language processing for local, colloquial queries
• Voice input/output for low-literacy users
• Image analysis for crop disease identification
• Multi-language support (Hindi, English, and regional languages)

📊 Comprehensive Data Integration

• Weather data and forecasting
• Soil health information
• Crop cycle recommendations
• Market price analysis
• Government policy updates
• Financial credit information

🎯 Core Capabilities

• Irrigation Timing: Optimal irrigation scheduling based on weather and soil conditions
• Crop Selection: Weather-adaptive seed variety recommendations
• Yield Protection: Early warning systems for adverse weather conditions
• Market Intelligence: Price trends and optimal selling timing
• Financial Guidance: Credit availability and government subsidy information

🔧 Technical Architecture

• Frontend: React-based responsive web application
• Backend: FastAPI with async processing
• AI/ML: Multi-modal LLM integration with grounding mechanisms
• Data Sources: Public datasets with proper attribution
• Offline Capability: Core features available without internet

Project Structure

AgriAI_Hackathon/
├── frontend/                 # React web application
├── backend/                  # FastAPI backend
├── ml_models/               # AI/ML models and data processing
├── data/                    # Public datasets and processed data
├── docs/                    # Documentation
└── deployment/              # Deployment configurations

Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Git

Installation

# Clone the repository
git clone <repository-url>
cd AgriAI_Hackathon

# Backend setup
cd backend
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py
uvicorn main:app --reload

# Frontend setup
cd ../frontend
npm install
npm install lucide-react
npm install react-markdown
npm start

Data Sources

This project uses publicly available datasets with proper attribution:

• Indian Meteorological Department (IMD) weather data
• Ministry of Agriculture crop statistics
• NABARD financial data
• State agricultural department datasets

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

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