AgriScan Backend

AI-powered crop disease diagnosis backend using Google Gemini Vision API and IPFS storage.

Features

• AI Image Analysis - Diagnose crop diseases using Gemini Vision API
• AI Chatbot - Agricultural advisor powered by Gemini Pro
• IPFS Storage - Decentralized image storage via NFT.Storage
• Blockchain Ready - Returns IPFS CIDs for on-chain storage

API Endpoints

Health Check

GET /health

Returns server status.

Analyze Image

POST /analyze
Body: {
  "imageBase64": "data:image/jpeg;base64,...",
  "plantType": "Tomato" (optional)
}

Returns diagnosis, confidence, and IPFS CID.

Chat

POST /chat
Body: {
  "message": "How do I treat early blight?",
  "context": "Tomato plant with brown spots" (optional)
}

Returns AI agricultural advice.

Setup

1. Install dependencies

   npm install

2. Configure environment variables

   cp env.example .env

   Edit .env and add:

   • GEMINI_API_KEY - Your Google Gemini API key
   • IPFS_API_KEY - Your NFT.Storage API key (optional)

3. Run locally

   npm start
   # or for development
   npm run dev

4. Test

   curl http://localhost:3000/health

Deployment

Vercel

This backend is configured for Vercel deployment:

1. Push to GitHub
2. Import to Vercel
3. Set environment variables in Vercel dashboard
4. Deploy!

See ../DEPLOY_TO_VERCEL.md for detailed instructions.

Environment Variables

• GEMINI_API_KEY - Required for AI analysis
• IPFS_API_KEY - Optional, for IPFS storage
• PORT - Server port (default: 3000)
• NODE_ENV - Environment (production/development)

Tech Stack

• Express.js - Web framework
• Google Gemini AI - Vision and chat models
• NFT.Storage - IPFS storage
• CORS - Cross-origin support

License

MIT