Arogya Krishi - AI Agriculture Platform

A modern web application for AI-powered crop disease detection, soil prediction, and fertilizer recommendations.

Architecture

• Backend: Flask with ML models (Python)
• Frontend: Next.js 15 (App Router) + TypeScript + Tailwind CSS + shadcn/ui

Quick Start

Prerequisites

• Python 3.8+
• Node.js 18+
• Flask backend dependencies
• ML model files in /model directory

1. Start the Backend

cd ArogyaKrishi
python app.py

The Flask server will start on http://localhost:5000

2. Start the Frontend

cd frontend
npm install
npm run dev

The Next.js app will start on http://localhost:3000

3. Access the Application

Open http://localhost:3000 in your browser. The app will automatically redirect to /dashboard.

Features

1. Disease Detection

• Upload plant leaf images
• AI-powered disease identification
• Confidence scores and fertilizer recommendations
• Image preview with results

2. Soil Prediction

• Upload soil images
• Automatic soil type classification
• Crop recommendations based on soil type
• Analysis details and next steps

3. Fertilizer Recommendations

• Input crop name and nutrient levels (N, P, K)
• Get personalized fertilizer advice
• Nutrient status indicators
• Detailed recommendations and analysis

API Endpoints

New JSON APIs (for Next.js frontend)

• POST /api/disease-detection - Disease detection with image upload
• POST /api/soil-prediction - Soil type prediction with image upload
• POST /api/fertilizer-recommendation - Fertilizer recommendations

Legacy Template APIs (still available)

• POST /predict - Disease detection (returns HTML)
• POST /soil-predict - Soil prediction (returns HTML)
• POST /fertilizer-predict - Fertilizer recommendations (returns HTML)

Frontend Structure

frontend/src/
  app/                    # Next.js App Router pages
    dashboard/           # Dashboard layout and feature pages
  components/
    layout/              # Sidebar, Navbar, AppShell
    ui/                  # shadcn/ui components
    common/              # Reusable components
    providers/           # React providers
  features/              # Feature-based modules
    disease-detection/
    soil-prediction/
    fertilizer-recommendation/
  lib/                   # Utilities
  store/                 # Zustand state management
  types/                 # TypeScript definitions

Development

Backend Development

• Flask app in ArogyaKrishi/app.py
• ML models loaded lazily on first use
• CORS enabled for frontend integration
• Session-based authentication (login/signup)

Frontend Development

• Next.js 15 with App Router
• TypeScript for type safety
• Tailwind CSS + shadcn/ui for styling
• React Query for API state management
• Zustand for client state
• React Hook Form + Zod for form validation

Environment Variables

Create .env.local in the frontend directory:

NEXT_PUBLIC_BACKEND_URL=http://localhost:5000

Deployment

Backend Deployment

• Use WSGI server (Gunicorn, uWSGI)
• Configure proper CORS for production domain
• Set up SSL/HTTPS
• Configure proper file upload limits

Frontend Deployment

• Build: npm run build
• Start: npm start
• Deploy to Vercel, Netlify, or any Node.js hosting

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is licensed under the ISC License.