A modern web application that makes gardening easier for everyone using AI. GrowEasy provides detailed instructions, progress tracking, and AI-powered photo analysis to help you grow healthy plants.

• Track individual plants and their health status
• Monitor planting dates and watering schedules
• AI Photo Analysis: Upload photos for EfficientNet-powered health assessment
• View detailed plant information and care history
• Growth stage tracking with timeline predictions

• Plan and organize your garden zones
• Track sunlight exposure and soil types
• Manage plant placement and garden layout
• Monitor zone-specific conditions

• Comprehensive database of plants with care instructions
• Search and filter by plant type, difficulty, and requirements
• Detailed growing guides and care tips
• Scientific information and best practices

GrowEasy/
├── frontend/                 # React TypeScript App
│   ├── src/
│   │   ├── components/      # React components
│   │   ├── services/        # API services
│   │   └── ...
│   └── package.json
├── backend/                  # Python Flask + EfficientNet
│   ├── app.py              # Flask server
│   ├── requirements.txt    # Python dependencies
│   ├── start_backend.py    # Startup script (with virtual env)
│   ├── venv/               # Python virtual environment
│   └── ...
└── README.md

• Node.js (version 14 or higher)
• Python (version 3.8 or higher)
• npm or yarn

# Install dependencies
npm install

# Start development server
npm start

The React app will be available at http://localhost:3000

# Navigate to backend directory
cd backend

# Run the startup script (recommended - creates virtual environment)
python start_backend.py

# OR for Windows users:
start_backend.bat

# OR manual setup with virtual environment:
python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Unix/Linux/Mac
pip install -r requirements.txt
python app.py

The Python backend will be available at http://localhost:5000

1. Frontend: Open http://localhost:3000
2. Backend Health: Visit http://localhost:5000/health
3. Test AI: Upload a plant photo in the "Your Plant" section

• Model: EfficientNet-B0 (pre-trained on ImageNet)
• Analysis: Plant health, growth stage, disease detection
• Processing: 224x224 pixel input, GPU acceleration support
• Accuracy: Confidence scoring for all predictions
• Environment: Isolated Python virtual environment for clean dependencies

• Growth Assessment: Current stage and timeline predictions
• Health Analysis: Overall plant health evaluation
• Disease Detection: Early blight, late blight, nutrient deficiencies
• Anomaly Detection: Pest issues, watering problems
• Recommendations: Actionable care advice

• Vegetables: Tomatoes, Lettuce, Peppers, Cucumbers, Carrots
• Herbs: Basil, Mint, Rosemary, Thyme, Oregano, Parsley
• Fruits: Strawberries, Blueberries, Raspberries
• Flowers: Sunflowers, Marigolds, Lavender

• React 18 with TypeScript
• Tailwind CSS for styling
• Lucide React for icons
• Create React App for development

• Flask web framework
• EfficientNet deep learning model
• PyTorch for AI processing
• OpenCV for image processing
• CORS for cross-origin requests
• Python Virtual Environment for dependency isolation

GET /health

POST /analyze
Content-Type: application/json

{
  "image": "data:image/jpeg;base64,...",
  "plantType": "Tomato",
  "plantedDate": "2024-03-15"
}

• npm start - Runs the React app in development mode
• npm test - Launches the test runner
• npm run build - Builds the app for production
• npm run eject - Ejects from Create React App

cd backend
python start_backend.py  # Auto-creates virtual environment and installs dependencies

The backend uses a Python virtual environment (venv/) for clean dependency management:

# Windows
venv\Scripts\activate
venv\Scripts\deactivate

# Unix/Linux/Mac
source venv/bin/activate
deactivate

Create .env files for custom configurations:

Frontend (.env):

REACT_APP_API_URL=http://localhost:5000

Backend (.env):

FLASK_ENV=development
MODEL_PATH=./models

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

cd backend
python start_backend.py  # Creates virtual environment
# Or manually:
python -m venv venv
source venv/bin/activate  # Unix/Linux/Mac
venv\Scripts\activate     # Windows
pip install -r requirements.txt
gunicorn app:app -b 0.0.0.0:5000

1. Backend not responding

   • Check if Python backend is running on port 5000
   • Verify virtual environment is activated
   • Check dependencies: pip install -r requirements.txt

2. AI analysis fails

   • Ensure EfficientNet model is loaded
   • Check GPU/CPU availability
   • Verify image format (JPEG/PNG)
   • Make sure virtual environment is active

3. Virtual environment issues

   # Remove and recreate virtual environment
   rm -rf venv  # Unix/Linux/Mac
   rmdir /s venv  # Windows
   python start_backend.py

4. CORS errors

   • Backend CORS is configured for localhost:3000
   • Check browser console for specific errors

5. Port conflicts

   • Frontend: Change port in package.json scripts
   • Backend: Modify port in app.py

• GPU Usage: Install CUDA for faster AI processing
• Image Quality: Use high-resolution photos for better analysis
• Batch Processing: Upload multiple photos for comprehensive analysis
• Virtual Environment: Keep dependencies isolated for better performance

• Frontend: http://localhost:3000
• Backend: http://localhost:5000/health

• Frontend: Browser console and terminal
• Backend: Flask logs in terminal

# Check if virtual environment is active
echo $VIRTUAL_ENV  # Unix/Linux/Mac
echo %VIRTUAL_ENV% # Windows

• CORS configured for development
• Input validation on all endpoints
• No sensitive data storage
• Secure image processing
• Isolated virtual environment

• Advanced AI: Fine-tuned plant-specific models
• Weather Integration: Local weather-based recommendations
• Community Features: Share garden progress and tips
• Mobile App: Native iOS and Android applications
• Smart Notifications: AI-powered care reminders
• Batch Analysis: Process multiple plants simultaneously
• Video Analysis: Real-time plant monitoring
• Docker Support: Containerized deployment

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

For support and questions:

• Check the troubleshooting section
• Review backend logs for errors
• Ensure virtual environment is properly set up
• Open an issue in the GitHub repository

🌱 Happy Gardening with AI! 🌱