Smart Plastic Bottle Redemption System

Welcome to the Smart Plastic Bottle Redemption System! This project integrates RFID authentication, Arduino-based servo motor control, load cell weight verification, and image classification using OpenCV and TensorFlow to create an automated bottle redemption machine.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Installation
• Usage
• Project Structure
• Contributing
• License
• Acknowledgements

Project Overview

The Smart Plastic Bottle Redemption System is designed to automate the process of accepting and classifying plastic bottles for recycling. The system uses RFID to authenticate users, a load cell to verify the weight of the bottle, and a pre-trained MobileNetV2 model to classify the object as a plastic bottle or reject it. Points are awarded to registered users, which they can view on a web interface.

Features

• RFID User Authentication: Ensures only registered users can use the machine.
• Load Cell Weight Verification: Accepts objects weighing between 17g-23g.
• Image Classification: Uses OpenCV and TensorFlow to classify objects.
• Servo Motor Control: Directs accepted bottles to an acceptance box and rejected items to a rejection box.
• User Points Database: Awards points to users for accepted bottles and provides a web interface to view points.
• User Guidance Display: An Arduino 16x2 LCD provides step-by-step instructions.

Technologies Used

• Arduino: For controlling servo motors and displaying messages.
• RFID: For user authentication.
• Load Cell: For weight verification.
• OpenCV: For capturing and processing images.
• TensorFlow: For image classification using a pre-trained MobileNetV2 model.
• Python: Backend logic for image processing and machine learning.
• Xampp: For the web interface to display user points.
• SQLite: For user and points database.

Installation

Hardware Setup

1. Arduino and RFID Setup:

   • Connect the RFID reader to the Arduino.
   • Connect the servo motors to the Arduino.
   • Connect the 16x2 LCD display to the Arduino.
   • Connect the load cell to the Arduino using an HX711 amplifier.

2. Camera Setup:

   • Ensure your laptop’s built-in camera or an external camera is set up for image capture. Software Setup

3. Clone the Repository:
   

   https://github.com/chamishkadilina/Smart-Plastic-Bottle-Redemption-System.git

4. Set Up Python Environment:

5. Arduino Code:

   • Upload the arduino/arduino_code.ino to the Arduino board using the Arduino IDE.

6. Configure Database:

Usage

1. Start the Arduino Program:
2. Run the Main Python Program:
3. 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.

Project Structure

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

Contributing

We welcome contributions! Please read our Contributing Guidelines for more information.

License

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

Acknowledgements

• 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.