GreenGrow

📋 Problem Statement

GreenGrow – An AI-Powered Farming Assistant Platform

GreenGrow is a comprehensive agricultural advisory platform that leverages artificial intelligence to help farmers make informed decisions about crop cultivation, disease management, weather patterns, and market prices. It's designed to bridge the gap between traditional farming knowledge and modern AI technology, making advanced agricultural insights accessible to farmers everywhere.

🎬 Demo

🎯 Click to explore the live experience of GreenGrow's user-friendly interface and advanced AI-powered features.

📋 Features Overview

🤖 AI-Powered Chat Assistant

Text-based queries: Ask questions about crop cultivation, pest management, soil health, and farming best practices
Context-aware responses: AI understands your location and provides location-specific advice
Multi-turn conversations: Maintain conversation context for better assistance

🖼️ Image-Based Disease Detection

Upload crop images for instant disease diagnosis
Powered by TensorFlow deep learning model
Detects 15+ plant diseases across Pepper, Potato, and Tomato crops
Integration with Google Gemini Vision API for enhanced analysis
Provides treatment recommendations and prevention tips

🎤 Voice Assistant

Live voice interaction with AI farming advisor
Voice command processing for hands-free operation
Real-time context injection from multiple data sources
Natural language understanding for farming queries

🌤️ Weather Information

Real-time weather forecasts for your location
7-day weather predictions
Weather alerts and notifications
Location-based weather data integration
Interactive weather widgets

📊 Market Prices (Mandi Rates)

Real-time agricultural commodity prices
Multiple mandi (market) information
Price trends and historical data
Crop-specific market insights
Help farmers make informed selling decisions

🌾 Crop Management

Crop recommendations based on location and season
Detailed crop information and growing guides
Pest and disease management for specific crops
Seasonal planting calendars
Farm data tracking and management

🏛️ Government Schemes

Information about available agricultural schemes
Eligibility criteria and application processes
Scheme benefits and requirements
Location-based scheme recommendations

📝 Farm Profile Management

Create and manage farm profiles
Track farm statistics and metrics
Store farm location and details
View farm-specific recommendations

🔔 Community & Support

Community forum for farmer discussions
Help center with FAQs and guides
Support system for technical assistance
Knowledge sharing platform

⚙️ Settings & Personalization

User profile management
Notification preferences
Location settings
Theme and display preferences

🔐 Authentication & Security

Secure user registration and login
JWT-based authentication
Protected routes and API endpoints
User session management

🛠️ Tech Stack

Frontend

React 18 with TypeScript
Vite for fast development and building
Tailwind CSS for modern, responsive UI
React Router for navigation
Lucide React for icons
Axios for API calls
VAPI AI for voice assistant integration

Backend

Node.js with Express.js
MongoDB with Mongoose for database
JWT for authentication
Multer for file uploads
CORS enabled for cross-origin requests
Morgan for HTTP request logging

AI & ML

Google Gemini 2.0 Flash for chat and vision analysis
TensorFlow/Keras for disease detection model
Flask for Python ML backend
Axicov for AI workflow management

APIs & Services

OpenWeatherMap API for weather data
Government Mandi APIs for market prices
News APIs for agricultural news

🚀 Getting Started

Prerequisites

Node.js (v18 or higher)
Python 3.8+ (for Flask backend)
MongoDB (local or cloud instance)
Git

Installation Steps

1. Clone the Repository

git clone https://github.com/your-username/GreenGrow.git

# Navigate to the project directory
cd GreenGrow

2. Backend Setup (Node.js Server)

# Navigate to server directory
cd server

# Install dependencies
npm install

# Create .env file (copy from .env.example if available)
# Add your environment variables:
# MONGODB_URI=your-mongodb-connection-string
# JWT_SECRET=your-jwt-secret-key
# GEMINI_API_KEY=your-google-gemini-api-key
# OPENWEATHER_API_KEY=your-openweather-api-key
# FLASK_API_URL=http://localhost:5001

# Start the backend server
npm run dev
# Or for production
npm start

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

3. Flask Backend Setup (Disease Detection)

# Navigate to backend directory
cd backend

# Create virtual environment (Windows)
python -m venv venv
venv\Scripts\activate

# Or on Linux/Mac
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Or use the setup script
# Windows: setup_venv.bat
# Linux/Mac: chmod +x setup_venv.sh && ./setup_venv.sh

# Start Flask server
python app.py

The Flask server will start on http://localhost:5001

4. Frontend Setup (React Client)

# Open a new terminal and navigate to Client directory
cd Client

# Install dependencies
npm install

# Create .env file if needed
# VITE_API_URL=http://localhost:5000/api

# Start the development server
npm run dev

The frontend will start on http://localhost:5173 (or another port if 5173 is busy)

5. Environment Variables

Create .env files in both server and Client directories:

server/.env:

MONGODB_URI=mongodb://localhost:27017/greengrow
JWT_SECRET=your-super-secret-jwt-key-here
GEMINI_API_KEY=your-google-gemini-api-key
OPENWEATHER_API_KEY=your-openweather-api-key
FLASK_API_URL=http://localhost:5001
AXICOV_API_KEY=your-axicov-api-key (optional)
AXICOV_API_BASE=https://api.axicov.com/v1 (optional)

Client/.env:

VITE_API_URL=https://greengrow-n9g5.onrender.com/api

Running the Complete Application

Start MongoDB (if running locally)
Start Flask backend: cd backend && python app.py
Start Node.js server: cd server && npm run dev
Start React frontend: cd Client && npm run dev
Open http://localhost:5173 in your browser

📁 Project Structure

GreenGrow/
├── backend/              # Flask backend for disease detection
│   ├── app.py           # Flask application
│   ├── requirements.txt # Python dependencies
│   └── venv/            # Python virtual environment
├── Client/              # React frontend
│   ├── src/
│   │   ├── components/  # React components
│   │   ├── pages/       # Page components
│   │   ├── context/     # React context providers
│   │   ├── hooks/       # Custom React hooks
│   │   └── lib/         # Utility libraries
│   ├── package.json
│   └── vite.config.ts
├── server/              # Node.js backend
│   ├── src/
│   │   ├── routes/      # API routes
│   │   ├── controllers/ # Route controllers
│   │   ├── models/      # Database models
│   │   ├── middleware/  # Express middleware
│   │   ├── services/    # Business logic services
│   │   └── config/      # Configuration files
│   ├── package.json
│   └── uploads/         # Uploaded files directory
├── model/               # ML model files
│   └── disease_model/   # TensorFlow model
└── Readme.md           # Project documentation

🔌 API Endpoints

Authentication

POST /api/auth/register - User registration
POST /api/auth/login - User login
POST /api/auth/logout - User logout
GET /api/auth/me - Get current user

Chat & AI

POST /api/chat/message - Send text message to AI assistant
POST /api/chat/image-analysis - Upload image for disease detection
POST /api/chat/voice-command - Process voice commands
POST /api/chat/live-voice - Live voice assistant session

Market & News

GET /api/mandi - Get market prices
GET /api/news - Get agricultural news

Health Checks

GET /api/health - Node.js server health
GET /health - Flask server health (port 5001)

🧪 Testing

# Test backend API
cd server
npm test

# Test frontend
cd Client
npm test

📦 Deployment

Backend Deployment

cd server
npm run build
npm start

Frontend Deployment

cd Client
npm run build
# Deploy the 'dist' folder to your hosting service

Flask Backend Deployment

The Flask backend can be deployed using:

Heroku
AWS Elastic Beanstalk
Google Cloud Run
Railway
Render

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

Google Gemini API for AI capabilities
TensorFlow team for ML framework
OpenWeatherMap for weather data
All open-source contributors

Made with ❤️ by the GreenGrow Team

Name		Name	Last commit message	Last commit date
Client		Client
backend		backend
model		model
server		server
.gitignore		.gitignore
Readme.md		Readme.md
axicov.config.js		axicov.config.js
package-lock.json		package-lock.json
package.json		package.json
update.js		update.js
update_chat.js		update_chat.js
update_panel.js		update_panel.js

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