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.
- TensorFlow Lite Community - for providing robust tools and documentation, enabling seamless integration of machine learning on mobile platforms.
- Android Open Source Community - for sharing invaluable resources and frameworks.
- Bangladesh Agricultural University - for supporting the project's research foundation.
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.
- 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.
To deploy the Orange Classification Android App, follow these steps:
- Clone the Repository:
git clone https://github.com/myself-aas/Orange_Classification_Android_App.git-
Open the Project: Open Android Studio and select
Open an existing Android Studio project.Navigate to the cloned repository and select it. -
Build the Project: Ensure all dependencies are synced by clicking on
Sync Project with Gradle Files. -
Run the App: Connect your Android device or start an emulator. Click on the
Runbutton in Android Studio to deploy the app. -
Permissions: Ensure your app has the necessary permissions for camera and storage access in the AndroidManifest.xml file.
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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.
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.
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.
For support, email shuvoasifahmed@gmail.com.