AgriMind

An AI-powered agricultural assistance platform combining plant disease detection and intelligent knowledge retrieval for farmers and agricultural professionals.

🌟 Features

• 🔬 Plant Disease Detection: AI-powered disease identification using Vision Transformer models
• 🧠 Agricultural Knowledge Assistant: RAG-powered Q&A system for farming guidance
• 🌐 Web Interface: Modern React frontend with image upload and voice input
• 🔗 Integrated Analysis: Combined image + query processing for comprehensive insights
• 🌾 Crop-Specific Insights: Specialized knowledge for Corn, Potato, Rice, and Wheat
• 📊 Market Intelligence: Real-time market data and price information
• 🗺️ Regional Expertise: Focused on West Bengal agriculture and practices

🚀 Quick Start

Option 1: Docker Compose (Recommended)

The easiest way to run AgriMind with all dependencies:

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

# Start everything with one command (includes databases!)
./start-agrimind.sh    # macOS/Linux
# OR
start-agrimind.bat     # Windows

# Or manually with docker-compose
docker-compose up --build

This will start:

• PostgreSQL database with pgvector extension
• Redis for caching
• AgriMind API server
• All services accessible at localhost

Option 2: Development Setup

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

# One-command setup (creates venv, installs dependencies, starts Docker)
pnpm setup        # macOS/Linux
# OR
pnpm setup:windows  # Windows

# Start the application
pnpm dev

Open http://localhost:3000 in your browser and start analyzing your crops!

🔑 Environment Setup

Important: You need a Gemini API key for the AI assistant to work properly.

1. Get your Gemini API key:

   • Go to Google AI Studio
   • Create a new API key (free tier available)

2. Set your API key:

   # Edit the environment file
   nano apps/rag-script/.env
   
   # Replace this line:
   GEMINI_API_KEY=your_gemini_api_key_here
   
   # With your actual key:
   GEMINI_API_KEY=your_actual_api_key_here

3. Optional configurations:

   • apps/api/.env - API server settings (defaults work fine)
   • apps/rag-script/.env - RAG system settings (defaults work fine)

Plant Disease Detection

Detect diseases in plant images with high accuracy:

# Detect disease in an image (human-readable output)
npm run detect-disease path/to/image.jpg

# Get JSON output for API integration
npm run detect-disease path/to/image.jpg -- --json

# Quiet mode (suppress loading messages)
npm run detect-disease path/to/image.jpg -- --quiet --json

Supported diseases: 13+ conditions across Corn, Potato, Rice, and Wheat including rusts, blights, spots, and healthy conditions.

Agricultural Knowledge Assistant

Get intelligent answers to farming questions:

# Interactive mode - ask questions interactively
npm run ask-agrimind

# Single query with human-readable output
npm run ask-agrimind -- --query "What are the best crops for West Bengal during Kharif season?"

# Get JSON output for API integration
npm run ask-agrimind -- --query "Rice prices in Kolkata" --format json

# Market-specific queries
npm run ask-agrimind -- --query "Current vegetable prices" --type market

# Regional queries with filters
npm run ask-agrimind -- --query "Farming practices in Murshidabad" --region "Murshidabad"

Knowledge base includes: ICAR reports, market data, farming advisories, weather patterns, and crop recommendations.

📋 Requirements

• Python 3.8+
• Node.js 18+ and pnpm
• Docker

That's it! The setup script handles everything else.

🔧 Manual Setup (if needed)

If you prefer manual setup or encounter issues:

1. Create Python virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install all dependencies:

   pnpm install
   pip install -r apps/api/requirements.txt
   pip install -r apps/rag-script/requirements.txt
   pip install -r apps/ml-inference/requirements.txt
   pip install -r packages/kb/requirements.txt

3. Start Docker services:

   Option A: Complete Stack (Recommended)

   # Start everything including databases
   docker-compose up --build

   Option B: Infrastructure Only

   docker compose -f infra/compose.yml up -d

4. Initialize RAG system (if using Option B):

   cd apps/rag-script && python setup_db.py && python load_knowledge_base.py && cd ../..

🏗️ Architecture

AgriMind/
├── apps/
│   ├── api/              # Backend API server
│   ├── frontend/         # Next.js web interface
│   ├── ml-inference/     # Disease detection service
│   └── rag-script/       # Knowledge retrieval service
├── packages/
│   ├── ui/               # Shared UI components
│   ├── kb/               # Knowledge base processing
│   └── typescript-config/ # Shared TypeScript configs
└── infra/
    └── compose.yml       # Docker services

🧪 Test Your Setup

# Check if environment variables are set correctly
pnpm check-env

# Test disease detection
npm run detect-disease apps/ml-inference/test_leaf.jpg

# Test RAG system (requires Gemini API key)
npm run ask-agrimind -- --query "What crops are good for West Bengal?"

# Check system health
npm run ask-agrimind -- --health-check

� Docker Setup

AgriMind includes a complete Docker setup with databases for easy deployment:

Quick Start with Docker

# Start the complete stack (recommended)
./start-agrimind.sh    # macOS/Linux
start-agrimind.bat     # Windows

# Or manually
docker-compose up --build

Services Included

• PostgreSQL: Database with pgvector extension for embeddings
• Redis: Caching and session management
• AgriMind API: Complete backend with all services

Access Points

• API Server: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Health Check: http://localhost:8000/health
• Database: localhost:5432 (user: agrimind, password: agrimind)
• Redis: localhost:6379

Docker Commands

# Start services
docker-compose up --build

# View logs
docker-compose logs -f api

# Stop services
docker-compose down

# Rebuild specific service
docker-compose build api && docker-compose up -d api

For detailed Docker setup information, see DOCKER_SETUP.md.

�🔧 Development

# Start all services in development mode
pnpm dev

# Run linting across all packages
pnpm lint

# Build all packages
pnpm build

📖 Documentation

• Plant Disease Detection - Detailed ML inference documentation
• RAG System - Knowledge retrieval system guide
• Knowledge Base Processing - Data processing pipeline
• Frontend - Web interface documentation