https://github.com/myself-aas/orange_classification_android_app
Android app that uses a TensorFlow Lite model for image classification of different type of oranges, trained through Google's Teachable Machine.
https://github.com/myself-aas/orange_classification_android_app
android android-app android-application android-development android-studio android-ui image-classification machine-learning object-detection teachable-machine tensorflow-lite
Last synced: 3 months ago
JSON representation
Android app that uses a TensorFlow Lite model for image classification of different type of oranges, trained through Google's Teachable Machine.
- Host: GitHub
- URL: https://github.com/myself-aas/orange_classification_android_app
- Owner: myself-aas
- Created: 2024-10-29T19:03:09.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-30T18:01:47.000Z (about 1 year ago)
- Last Synced: 2025-04-05T07:09:39.300Z (10 months ago)
- Topics: android, android-app, android-application, android-development, android-studio, android-ui, image-classification, machine-learning, object-detection, teachable-machine, tensorflow-lite
- Language: Java
- Homepage:
- Size: 38.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Orange Classification Android App: Real-Time Fruit Classification
This Android app uses TensorFlow Lite to classify oranges directly on-device. Developed with Android Studio and written in Java, it enables real-time, low-latency classification via a user-friendly interface, ideal for agriculture and food applications. Features include real-time classification, and a seamless UI. Built for Android 5.0 (Lollipop) or higher, this project is open-source and designed for easy deployment and collaboration.
## Acknowledgements
- [TensorFlow Lite Community](https://www.tensorflow.org/community) - for providing robust tools and documentation, enabling seamless integration of machine learning on mobile platforms.
- [Android Open Source Community](https://source.android.com/) - for sharing invaluable resources and frameworks.
- [Bangladesh Agricultural University](https://bau.edu.bd/) - for supporting the project's research foundation.
## Documentation
The Orange Classification Android App is designed to classify oranges based on various parameters using machine learning techniques. This section provides an overview of the app's components and functionalities:
- **MainActivity.java**: The entry point of the application, managing the user interface and interactions.
- **CameraFragment**: Handles camera operations and image capture for classification.
- **ViewModel**: Manages UI-related data in a lifecycle-conscious way, ensuring data survives configuration changes.
- **ML Model**: Utilizes a pre-trained model for orange classification, providing accurate results based on the input images.
For detailed technical documentation, refer to the `inline comments` in the code files.
## Screenshots
## Features
- **Image Classification:** Accurately classify different types of oranges using machine learning algorithms.
- **User-Friendly Interface:** Intuitive design for easy navigation and interaction.
- **Real-Time Processing:** Quickly analyze images captured by the device's camera for immediate results.
- **Offline Functionality:** Perform classification without the need for an internet connection.
- **Support for Multiple Devices:** Compatible with a wide range of Android devices running version 5.0 (Lollipop) and above.
- **Lightweight and Efficient:** Optimized for performance and low resource consumption.
## Deployment
To deploy the Orange Classification Android App, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/myself-aas/Orange_Classification_Android_App.git
```
2. **Open the Project**:
Open Android Studio and select `Open an existing Android Studio project.`
Navigate to the cloned repository and select it.
3. **Build the Project**:
Ensure all dependencies are synced by clicking on `Sync Project with Gradle Files.`
4. **Run the App**:
Connect your Android device or start an emulator.
Click on the `Run` button in Android Studio to deploy the app.
5. **Permissions**:
Ensure your app has the necessary permissions for camera and storage access in the AndroidManifest.xml file.
6. **Testing**:
Test the app on various devices to ensure compatibility and functionality.
`This section provides clear, step-by-step instructions for deploying your app, making it easier for users to get started.`
# Hi, I'm Ashif A.! 👋
## 🚀 About Me
I am Ashif Ahmed Shuvo, a passionate Android developer with a keen interest in artificial intelligence and machine learning. My goal is to leverage technology to create innovative solutions that enhance everyday experiences. With a strong foundation in programming and a commitment to continuous learning, I strive to contribute to impactful projects.
## 🔗 Links
[](https://myself-aas.github.io/portfolio/)
[](https://www.linkedin.com/in/me-aas/)
[](https://x.com/myself_aas)
## FAQ
**Q1: What is the purpose of the Orange Classification app?**
A1: The Orange Classification app is designed to classify oranges using machine learning techniques based on images captured through the device's camera.
**Q2: How do I install the app?**
A2: Clone the repository and import it into Android Studio. Ensure you have the necessary dependencies and SDK installed, then build and run the app.
**Q3: What are the system requirements?**
A3: The app requires an Android device running Android 5.0 (Lollipop) or higher with camera functionality.
**Q4: How can I contribute to this project?**
A4: Contributions are welcome! Please fork the repository, make your changes, and submit a pull request for review.
**Q5: Where can I find more information about the machine learning model?**
A5: Detailed information about the machine learning model can be found in the `inline comments` within the code.
## Authors
- [Ashif Ahmed Shuvo](https://github.com/myself-aas)
## Badges
[](https://github.com/myself-aas/Orange_Classification_Android_App/stargazers)
[](https://github.com/myself-aas/Orange_Classification_Android_App/issues)
[](https://linkedin.com/in/me-aas/)
[](https://github.com/myself-aas)
## Support
For support, email shuvoasifahmed@gmail.com.
## License
[MIT](https://choosealicense.com/licenses/mit/)