TeaOptima

Research system for analyzing tea leaf quality and forecasting quality degradation using hybrid AI/ML models integrated with weather and environmental data, accessible via a mobile interface.

                   

Publication

The research paper behind TeaOptima has been published in the IEEE Xplore proceedings of the MERCon-Moratuwa Engineering Research Conference 2025 :

R. J. Bandara and A. Kuruppu, “Predictive Analytics for Tea Leaf Aging and Quality Degradation,” 2025 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, pp. 239–244, 2025.

📄 Read the paper on IEEE Xplore

UI Samples

Features

• 📸 Image Classification:

  • Capture high-resolution images of tea leaves directly from the app.
  • Utilize advanced machine learning models to analyze and classify the quality of tea leaves based on visual characteristics.

• 🔮 Degradate Prediction:

  • Leverage AI-powered algorithms to predict the degradation of tea leaves over time.
  • Provide users with insights into the expected quality changes, helping in better inventory and quality management.

• 🌤️ Weather Integration:

  • Integrate real-time weather data to assess its impact on tea quality.
  • Use OpenWeatherMap API to fetch current weather conditions and forecasts, influencing tea leaf quality predictions.

• 📊 Data Visualization:

  • Display comprehensive charts and graphs to visualize quality trends over time.
  • Enable users to track historical data and make informed decisions based on visual analytics.

• 🔥 Firebase Integration:

  • Implement secure user authentication using Firebase Auth.
  • Store user data and app configurations in Firebase Firestore for seamless data management and retrieval.

• 📱 Cross-Platform:

  • Ensure compatibility across iOS, Android, and Web platforms.
  • Provide a consistent user experience regardless of the device or platform used.

Setup Instructions

Prerequisites

• Flutter SDK (3.7.0+)
• Firebase project
• OpenWeatherMap API account (optional)

1. Clone the Repository

git clone https://github.com/RavJayB/tea-leaf-quality-prediction.git
cd tea-quality-app

2. Install Dependencies

flutter pub get

3. Environment Configuration

⚠️ Required: You must create a .env file with the required API key.

1. Copy the environment template:

   cp .env.example .env

2. Edit .env and replace placeholder values:

   # Required - Get from https://openweathermap.org/api
   OPENWEATHER_API_KEY=your_actual_openweather_api_key
   
   # Optional - Override default service URLs if needed
   IMAGE_SERVICE_URL=your_image_service_url   # contains YOLOv8 Model 
   DEGRADE_SERVICE_URL=your_degrade_service_url  # contains Random Forest Model

Note: The app will crash on startup if OPENWEATHER_API_KEY is missing. Service URLs have fallback values for development.

4. Firebase Configuration

1. Copy the Firebase template:

   cp lib/firebase_options.example.dart lib/firebase_options.dart

2. Replace placeholder values in lib/firebase_options.dart with your Firebase project credentials:

   • Get these from Firebase Console > Project Settings > General tab
   • Replace all YOUR_* placeholders with actual values

3. Download and place platform-specific config files:

   • Android: google-services.json → android/app/
   • iOS: GoogleService-Info.plist → ios/Runner/

5. API Keys Setup

OpenWeatherMap API (Required)

1. Sign up at OpenWeatherMap
2. Get your free API key
3. Add it to your .env file - this is required for the app to run properly

Backend Services

If you have your own ML services, update the URLs in .env:

• IMAGE_SERVICE_URL: Your image classification endpoint
• DEGRADE_SERVICE_URL: Your quality prediction endpoint

6. Run the App

flutter run

Project Structure

lib/
├── main.dart                 # App entry point
├── firebase_options.dart     # Firebase configuration (excluded from git)
├── screens/                  # UI screens
├── services/                 # Business logic and API calls
│   ├── api_service.dart      # ML service integration
│   ├── auth_service.dart     # Firebase authentication
│   └── config_service.dart   # Environment configuration
└── widgets/                  # Reusable UI components

Security Considerations

This project is configured for safe public repository sharing:

• ✅ API keys are externalized to .env files
• ✅ Firebase configuration uses templates
• ✅ Sensitive files are in .gitignore
• ✅ Fallback values for development

Never commit real API keys or credentials to version control.

Development

Adding New Environment Variables

1. Add the variable to .env.example
2. Update lib/services/config_service.dart
3. Use ConfigService.yourVariable in your code

Firebase Setup

If you need to reconfigure Firebase:

flutterfire configure

Troubleshooting

Common Issues

1. "Missing OPENWEATHER_API_KEY": Create a .env file with your OpenWeatherMap API key
2. "Classification failed (500)": Backend service is down or misconfigured
3. Firebase initialization errors: Check firebase_options.dart configuration
4. Weather data not loading: Verify OpenWeatherMap API key in .env
5. Build errors: Run flutter clean && flutter pub get

Getting Help

• Check the Flutter documentation
• Review Firebase setup guides
• Ensure all API keys are correctly configured
• For any questions or collaboration, Contact me through my portfolio: ravindubandara.dev

Contributing

1. Fork the repository
2. Create a feature branch
3. Follow the security guidelines for API keys
4. Submit a pull request

License

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