# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 6 Conclusions  
This paper outlines the development and successful testing of an AI-driven recycling sorter, marking a significant advancement in automated waste management, by integrating a YOLOv8-based deep learning model with a PLC-controlled mechanical system. The developed prototype demonstrated effective classification of recyclable materials, including plastics, metals, and paper. It achieves a precision rate of 90% and a recall rate of 80%, with a mean Average Precision at 50% threshold (mAP50) of 86%, ensuring rapid and accurate material segregation. This seamless integration ensures reliable real-time functionality, suitable for broad waste management applications. In continuous use, it maintained high precision and reliability, showing low training losses, a box loss of 0.6, and a classification loss of 0.5, effectively handling object overlaps without overfitting. Compared to existing solutions, such as those reviewed in the literature, our system shows excellent performance. For example, compared to the ResNet50 model evaluated by [PERSON] et al. [37], which achieved an accuracy of 95% and an F1 score of 94.8%, our system provides comparable accuracy but with enhanced real-time processing capabilities essential for dynamic waste sorting environments. This represents a clear improvement in operational efficiency and adaptability, with our system achieving an mAP50 approximately 23% higher than similar AI models discussed in a recent study by [PERSON] et al. [PERSON] et al. (2019). The integration of advanced AI capabilities not only mitigates the limitations of traditional sorting methods but also enhances the precision and throughput of the sorting. Unlike traditional methods that struggle with mixed waste and require extensive human intervention, our AI-enhanced system reduces labor costs, minimizes human error, and improves overall operational efficiency. This reduction in dependence on manual labor and the improvement of material quality are crucial as global waste volumes continue to challenge the urban and industrial sectors. Looking ahead, the system's scalability and adaptability will enable compliance with stringent environmental and regulatory requirements, adjusting efficiently to diverse waste categories and increasingly intricate waste management contexts. This AI-driven platform plays an instrumental role in the promotion of sustainable waste management, minimizing environmental impacts, and serving the principle of the circular economy. It stands out not only for its technical capabilities but also for its significant contributions to resource conservation and environmental preservation. In conclusion, this AI-powered sorting system not only showcases the technical capabilities and significant impacts of AI in waste sorting but also sets new standards for sustainable waste management solutions aligned with evolving regulatory and environmental standards. Conceptualization, V.R., M.S., A.N., C.K. and N.R. ; methodology, N.A. and N.R. ; software, V.R., M.S., A.N. and C.K. ; validation, N.A., M.R., H.E. and N.R. ; formal analysis, N.A., V.R., M.S., A.N., C.K. and N.R. ; investigation, V.R., M.S., A.N. and C.K. ; resources, N.R. ; data curation, V.R., M.S., A.N., C.K. and N.R. ; writing--original draft preparation, N.A., V.R., M.S., A.N., C.K. and N.R. ; writing--review and editing, N.A., M.R., H.E. and N.R. ; visualization, N.A., V.R., M.S., A.N., C.K., M.R., H.E. and N.R. ; supervision, N.A. and N.R. ; project administration, N.R. ; funding acquisition, N.A. and N.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the mechatronics laboratories at the College of Computing, Department of Applied Computing, Michigan Technological University. Not applicable. The data are available upon request from the corresponding authors. This research was conducted in the Advanced Programmable Logic Controllers Laboratory within the Department of Applied Computing at the College of Computing, Michigan Technological University.