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A smart recycling object identification system in vending machines based on edge computing platform and Petri net
Yi-Nan Lin1, Hsiao-Chieh Hsu1, Ya-Fen Wu1
1Department of Electronic Engineering, Ming Chi University of Technology, 84, Gongzhuan Rd., Taishan District, New Taipei City, 243, Taiwan.
Journal of Environmental Management
|March 6, 2026
Summary
This study introduces an AI-powered system for automated recycling sorting from vending machines. Utilizing object recognition, it enhances waste management efficiency and supports the circular economy.
Area of Science:
- Environmental Science
- Computer Science
- Robotics
Background:
- Growing global environmental concerns necessitate effective waste recycling solutions.
- The expanding vending machine market generates a significant volume of recyclable waste.
- Current manual sorting methods are inefficient for the increasing waste stream.
Purpose of the Study:
- To develop an automated system for sorting recyclable materials from vending machines.
- To leverage object recognition technology for improved waste management.
- To create a scalable, cost-effective solution for decentralized waste management.
Main Methods:
- Object recognition model YOLOv7-tiny trained for high accuracy (98.9% mAP).
- Integration with HUB8735 for camera-based object recognition and motor control.
- Petri net modeling for process verification and ensuring accuracy.
Main Results:
- Successful development and implementation of a recyclable vending machine prototype.
- Demonstrated high accuracy in identifying and sorting recyclable materials.
- Verified operational processes through mathematical modeling.
Conclusions:
- The AI-driven system offers a scalable and cost-effective alternative to manual waste sorting.
- Enhances resource recovery purity and reduces carbon footprint.
- Provides a practical framework for integrating AI into the circular economy.
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