Litrack is a cutting-edge iOS application designed to combat environmental pollution by empowering users to track and manage their waste disposal habits. Using advanced machine learning capabilities through CoreML, the app can automatically identify and classify different types of recyclable materials, making waste management smarter and more efficient.

• AI-Powered Classification - Automatically identify plastic, cans, and glass using CoreML
• Visual Analytics - Track your waste disposal patterns with beautiful charts
• Cloud Sync - Seamlessly sync data across devices with iCloud integration
• Smart Camera - Capture and classify waste items in real-time
• Persistent Storage - Reliable local data storage with CoreData

Utilize your device's camera to instantly identify and classify waste materials using advanced CoreML models. The app processes images on-device for privacy and speed, providing immediate feedback on waste type classification.

Technical Details:

• On-device CoreML inference
• Real-time image processing
• Support for Plastic, Can, and Glass classification
• Privacy-focused (no data sent to servers)

Visualize your waste tracking data with interactive charts powered by the Charts library. Understand your environmental impact through beautiful visualizations that show your recycling patterns over time.

Features Include:

• Interactive bar, line, and pie charts
• Historical data analysis
• Waste type distribution
• Tracking trends and patterns

Keep your waste tracking data synchronized across all your Apple devices with NSPersistentCloudKitContainer. Your data is automatically backed up and available wherever you need it.

Benefits:

• Automatic cross-device sync
• Secure cloud backup
• Conflict resolution
• Seamless data migration

Clean, modern UI built with UIKit, providing a seamless user experience for waste tracking and management. The interface is designed to be simple yet powerful, making waste tracking effortless.

Design Principles:

• User-friendly navigation
• Clear visual feedback
• Accessible design
• Consistent iOS patterns

• ViewController - Main application interface
• CameraViewController - Camera capture and ML classification
• Storyboards - UI layouts and navigation flows

• CoreML Models - Waste classification intelligence
• View Controllers - Application flow control
• Data transformation and validation

• CoreData Stack - Local persistence
• CloudKit Container - Cloud synchronization
• Data models and entities

1. Launch → User opens the app and sees the main dashboard
2. Capture → Camera captures waste item for classification
3. Classify → CoreML model identifies the waste type (Plastic/Can/Glass)
4. Store → Data is saved to CoreData and synced with iCloud
5. Analyze → View tracking statistics and charts

• macOS with Xcode 11 or later
• iOS 13.0+ deployment target
• Apple Developer account (for device testing)

1. Clone the repository

   git clone https://github.com/elmeeee/Litrack-MC2.git
   cd Litrack-MC2

2. Open project

   open Litrack-MC2.xcodeproj

3. Build and Run

   • Select your target device or simulator
   • Press Cmd + R to build and run

1. Launch the App - Open Litrack on your iOS device
2. Grant Permissions - Allow camera access for waste detection
3. Capture Waste - Use the camera to photograph recyclable items
4. View Classification - See AI-powered waste type identification
5. Track Progress - Monitor your waste tracking in the analytics dashboard

Litrack-MC2/
├── Litrack-MC2/                   # Main source code directory
│   ├── AppDelegate.swift           # App lifecycle management
│   ├── SceneDelegate.swift         # Scene configuration
│   ├── LitrackApp.swift            # SwiftUI app entry point
│   ├── DataController.swift        # CoreData stack management
│   ├── Core/                       # Core app components
│   │   └── App/                    # App-level configurations
│   ├── Features/                   # Feature modules
│   ├── Views/                      # SwiftUI views
│   │   ├── ContentView.swift       # Main content view
│   │   ├── HomeView.swift          # Home screen
│   │   ├── HistoryView.swift       # History tracking
│   │   └── SettingsView.swift      # App settings
│   ├── Models/                     # Data models
│   ├── Shared/                     # Shared utilities and components
│   ├── Camera/                     # Camera & ML integration
│   ├── Assets.xcassets/            # App assets and images
│   ├── Base.lproj/                 # Localization resources
│   │   ├── Main.storyboard         # Main UI layout
│   │   └── LaunchScreen.storyboard # Launch screen
│   └── Litrack_MC2.xcdatamodeld/   # CoreData model
├── Litrack-MC2.xcodeproj/         # Xcode project file
├── Litrack-MC2Tests/              # Unit tests
├── Litrack-MC2UITests/            # UI tests
├── docs/                          # Documentation and diagrams
├── .gitignore                     # Git ignore rules
└── README.md                      # This file

The app is built with the user in mind, featuring:

• Intuitive Navigation - Easy to understand flow from capture to analysis
• Clear Visual Feedback - Immediate confirmation of actions and classifications
• Minimal Learning Curve - Get started tracking waste in seconds
• Accessible to All - Designed following iOS accessibility guidelines

Ensuring smooth and efficient operation:

• Fast App Launch - Optimized initialization and resource loading
• Smooth Animations - 60 FPS UI transitions and interactions
• Efficient Memory Usage - Careful resource management
• Battery Conscious - Optimized ML inference and background operations

Your data stays yours:

• On-Device Processing - ML models run locally on your device
• Secure iCloud Sync - End-to-end encryption for cloud data
• No Data Sharing - Your waste tracking data is never shared
• User Data Protection - Compliance with iOS privacy standards

Making a positive environmental impact:

• Promote Recycling Habits - Encourage consistent waste tracking
• Environmental Awareness - Educate users about their impact
• Track Positive Impact - Visualize your contribution to sustainability
• Eco-Friendly Behavior - Gamify and reward green actions

• iOS Version: 13.0 or later
• Device: iPhone 6s or newer
• Storage: 50 MB free space
• Permissions: Camera access

• iOS Version: 14.0 or later
• Device: iPhone 8 or newer
• Storage: 100 MB free space
• Account: iCloud account for synchronization

• macOS: Catalina (10.15) or later
• Xcode: 11.0 or later
• Swift: 5.0 or later

• Enhanced ML Models - Improve classification accuracy
• More Waste Types - Add support for paper, metal, and organic waste
• Social Features - Share achievements and compete with friends
• Gamification - Reward system for consistent tracking
• Widget Support - iOS 14+ home screen widgets
• Dark Mode - Full dark mode support
• Localization - Multi-language support

Currently, there are no known issues. If you encounter any problems, please open an issue.

Elmee Developer | Python | CoreML 💻 💼

Karina Widyastuti Swift | Python | CoreData 💻 💼 🐦 📷

H.A.I.N Wahyoe Design | Project Manager 💼 📷

Vioni Febiyanti Design | Keynote Speaker 💼 🎨 📷

Adlia Nailul Afifah Design | Keynote Speaker 📺 📷

Litrack contributes to environmental sustainability by:

• Raising Awareness - Helping users understand their waste patterns and recycling habits
• Promoting Recycling - Making it easy to track and identify recyclable materials
• Data-Driven Decisions - Providing insights for better waste management practices
• Community Building - Connecting eco-conscious individuals (future feature)
• Positive Habits - Encouraging consistent recycling behavior through tracking

By using Litrack, you're taking an active role in reducing environmental pollution and promoting a more sustainable future.

• Apple CoreML Documentation - Learn about machine learning on iOS
• CoreData Programming Guide - Master data persistence
• CloudKit Documentation - Understand cloud synchronization
• Charts Library GitHub - Explore data visualization options

• Swift Programming Language Guide - Official Swift documentation
• iOS Human Interface Guidelines - Design best practices
• Machine Learning Best Practices - Apple's ML resources

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

• Apple for CoreML and CoreData frameworks
• Charts library for beautiful visualizations
• The open-source community for inspiration and support