A smart recycling object identification system in vending machines based on edge computing platform and Petri net...

Due to the deteriorating global environment, waste recycling has become one of the key issues for environmental protection. According to the market research on vending machines by Global Information, Inc. (GII), the market size of vending machines is expected to continue expanding in the future, which also means that the issue of recyclable objects generated from vending machines will become more pressing. In response to this trend, this study focuses on the recycling of the most common products found in vending machines and proposes the development of a system that uses object recognition to assist humans in sorting recyclable materials from vending machines. This not only presents a new business opportunity for vending machine companies but also contributes to environmental protection. The recycling object identification system utilizes YOLOv7-tiny as a training model, using the best-trained weight file with a mean Average Precision (mAP) of 98.9% across all categories. The system is further integrated with HUB8735, which uses camera-based object recognition to control motors for sorting. The operational processes have been modeled and verified using Petri nets, a mathematical and graphical modeling tool, to ensure their accuracy and completeness. Finally, a recyclable vending machine was successfully built and detailed discussion on its operations has been presented. Beyond technical implementation, this research offers a scalable and cost-effective solution for decentralized waste management, demonstrating a significant economic advantage over manual labor. By enhancing the purity of recovered resources and minimizing carbon footprints associated with improper waste disposal, this system provides a practical framework for integrating AI-driven automation into the global circular economy.