AgroNavis

AI-powered smart farm monitoring platform — crop disease detection, farm management, and weather intelligence as a Progressive Web App.

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Overview

AgroNavis is an open-source PWA that helps farmers monitor crops, detect plant diseases via YOLOv8 AI, manage multiple farms with GPS precision, and receive hyper-local weather-based irrigation recommendations. It supports English, Hindi, and Bengali and works offline.

Services

Service	Stack	Port
Frontend	Next.js 14, TypeScript, Tailwind CSS	3000
Backend API	Express.js, TypeScript	3001
ML Service	FastAPI, YOLOv8, PyTorch	8001
Database	Supabase (PostgreSQL + RLS)	—

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Prerequisites

• Node.js 18+
• Python 3.9+
• Docker & Docker Compose (for Docker setup)
• Supabase account
• OpenWeatherMap API key

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Quick Start

Option A — Docker (recommended)

git clone https://github.com/YOUR_ORG/agronavis.git
cd agronavis

cp .env.example .env
cp frontend/.env.local.example frontend/.env.local
cp backend/.env.example backend/.env
# Fill in your credentials in each file

docker-compose up -d --build

Access: Frontend http://localhost:3000 | API http://localhost:3001 | ML http://localhost:8001

Option B — Local development

git clone https://github.com/YOUR_ORG/agronavis.git
cd agronavis

# Install all dependencies
npm run install:all

# Copy and fill env files
cp .env.example .env
cp frontend/.env.local.example frontend/.env.local
cp backend/.env.example backend/.env

# Terminal 1 — frontend + backend
npm run dev

# Terminal 2 — ML service
cd backend/ml-service
python -m venv venv && source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
python main.py

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Environment Variables

Root / Backend (backend/.env)

SUPABASE_URL=
SUPABASE_ANON_KEY=
SUPABASE_SERVICE_KEY=
DATABASE_URL=postgresql://postgres:password@localhost:5432/agronavis
JWT_SECRET=
CORS_ORIGIN=http://localhost:3000
PORT=3001
NODE_ENV=development
WEATHER_API_KEY=
ML_SERVICE_URL=http://localhost:8001

Frontend (frontend/.env.local)

NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
NEXT_PUBLIC_API_BASE_URL=http://localhost:3001/api
NEXT_PUBLIC_APP_URL=http://localhost:3000
NEXT_PUBLIC_WEATHER_API_KEY=

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Database Setup

1. Create a project at supabase.com.
2. Copy your Project URL and anon/service keys into the env files.
3. Run migrations:

cd backend
npm run db:link   # link to your Supabase project
npm run db:push   # apply all migrations

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Project Structure

agronavis/
├── frontend/               # Next.js PWA
│   └── src/
│       ├── components/     # UI components
│       ├── pages/          # Routes
│       ├── hooks/          # Custom React hooks
│       ├── auth/           # Auth logic
│       ├── utils/          # API helpers
│       └── styles/
├── backend/                # Express API
│   ├── src/
│   │   ├── routes/         # REST endpoints
│   │   ├── services/       # Business logic
│   │   ├── middleware/     # Auth, CORS
│   │   └── lib/            # DB client
│   ├── ml-service/         # FastAPI + YOLOv8
│   │   ├── main.py
│   │   ├── src/
│   │   └── models/         # Trained model weights (not committed)
│   └── supabase/
│       └── migrations/     # SQL migrations
├── .env.example
├── docker-compose.yml
└── package.json            # Monorepo scripts

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API Reference

All protected endpoints require Authorization: Bearer <token>.

Auth

POST /api/auth/register
POST /api/auth/login
GET  /api/auth/profile

Farms

GET    /api/farms
POST   /api/farms
GET    /api/farms/:id
PUT    /api/farms/:id
DELETE /api/farms/:id
GET    /api/farms/:id/details

Crops

GET    /api/crops
POST   /api/crops
GET    /api/crops/:id
PUT    /api/crops/:id
DELETE /api/crops/:id

AI Analysis

POST /api/ml/analyze-image         # base64 image
POST /api/ml/analyze-image-upload  # multipart file
GET  /api/ml/models/info

Other

GET /api/farmers, POST, PUT
GET /api/soil-health, POST
GET /api/resources, POST
GET /api/yields, POST

Error responses follow a consistent shape:

{ "success": false, "error": "message", "code": "ERROR_CODE" }

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Running Tests

# Backend
cd backend && npm test

# Frontend
cd frontend && npm test

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Deployment

Frontend — Vercel: push to main, import repo in Vercel, set env vars.

Backend — any Node.js host (Railway, Fly.io, Heroku). Set all backend env vars.

ML Service — any Python host or Docker container. Ensure model weights are present at backend/ml-service/models/.

Note: Model weight files (.pt, .onnx) are excluded from the repo via .gitignore. Download or train your own and place them in backend/ml-service/models/.

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Contributing

See CONTRIBUTING.md.

Security

See SECURITY.md.

License

MIT