This project involves a machine equipped with an RFID card reader to authenticate users. Users can insert bottles into the machine, which then classifies and detects if the item is a bottle. If it is a bottle, the user's points are updated on a webpage; if not, the machine rejects the item.

• Features
• Technologies Used
• Project Setup Guide
• Usage
• Project Structure
• License
• Acknowledgements
• Contributing

• RFID Authentication: Access control for registered users.
• Load Cell Verification: Checks weight (17g-23g) of objects.
• Image Classification: Uses OpenCV and TensorFlow to detect bottles.
• Servo Motor Control: Directs items based on classification results.
• User Points Database: Tracks user points, accessible via a web interface.

• Arduino, RFID, Load Cell, OpenCV, TensorFlow, Python, XAMPP, SQLite

• Refer the Project Setup Guide.md file for detailed instructions.

1. Launch XAMPP Control Panel and Start Apache and MySQL services.
2. In your browser, navigate to http:https://localhost/display_data.php to view the webpage.
3. Start the Arduino Program SmartRecycler.ino.
4. Run the Main Python Program arduino_communication.py.
5. User Interaction:
   • Users authenticate with their RFID cards.
   • Place the bottle on the load cell.
   • If the weight is correct, the camera captures an image and the classification process begins.
   • The servo motor directs the bottle to the appropriate box based on the classification.
   • Accepted bottles add points to the user's account, viewable on the web interface.

graph TD;
    RegisteredUser-->AuthenticateRFID;
    AuthenticateRFID-->DoorOpens;
    AuthenticateRFID-->DoorRemainsClosed;
    DoorOpens-->CheckWeightWithLoadCell;
    CheckWeightWithLoadCell-->RejectBox;
    CheckWeightWithLoadCell-->ProcessImageWithPythonOpenCV;
    ProcessImageWithPythonOpenCV-->RejectBox;
    ProcessImageWithPythonOpenCV-->RotateServoMotorToAcceptBox;
    RotateServoMotorToAcceptBox-->UpdateUserPointsInDatabase;
    UpdateUserPointsInDatabase-->DisplayUserPointsOnWebsite;

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

• Special thanks to our project team members.
  • CT/2020/027 - J.A.C.D.Kumara
  • CT/2020/047 - H.I.K.Jayarathna
  • CT/2020/065 - E.D.K.Chamara
  • ET/2020/010 - G.G.H.N. Kokilani
  • ET/2020/015 - P.C.Vithanage
  • ET/2020/098 - A.S.S.Sisiranatha
• Inspired by various open-source projects and tutorials on Arduino, OpenCV, and TensorFlow.

Contributions are welcome! If you find any issues or want to add new features, feel free to fork the repository and submit a pull request.