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Review | Green Intelligent Systems and Applications | Volume 5 - Issue 2 - 2025 | 123-139 | https://doi.org/10.53623/gisa.v5i2.706
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

A Systematic Literature Review of YOLO and IoT Applications in Smart Waste Management

Author(s): Trisna Gelar ORCID https://orcid.org/0000-0002-9915-7205 , Sofy Fitriani , Setiadi Rachmat ORCID https://orcid.org/0000-0002-0312-9892
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Computer Engineering and Informatics Department, Politeknik Negeri Bandung, West Java, Indonesia

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Green Intelligent Systems and Applications

Green Intelligent Systems and Applications

Volume 5 - Issue 2 - 2025

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The increase in urbanization and global population expansion resulted in increased garbage production, causing considerable environmental and public health issues that exceeded traditional waste management approaches. To tackle these challenges, automated waste detection and analysis integrated computer vision, especially deep learning, with the Internet of Things (IoT) in intelligent waste management applications. This comprehensive literature review investigated a wide range of You Only Look Once (YOLO) applications in IoT-based waste detection and management, demonstrating its efficacy in addressing global waste issues. Employing specific keywords and Boolean operators, the review followed a rigorous methodology to explore reputable electronic databases for peer-reviewed articles published from 2019 to 2025. The primary findings indicated that different iterations of YOLO (v3 to v12) were integrated with diverse IoT devices and computing setups, including edge and centralized systems. These integrations facilitated four crucial applications: hazardous waste management, monitoring of smart bins, classification of waste types, and detection of litter in public spaces. This integration enhanced sustainability through improved waste management practices, increased efficiency in waste processes, and reduced manual labor requirements. Challenges included precise waste identification in complex scenarios, adaptation to fluctuating environmental conditions, and ensuring dependable, low-power operation of IoT devices. To sum up, the integration of YOLO and IoT established a robust basis for intelligent waste management, transforming reactive approaches into proactive strategies. Moving forward, research should prioritize enhancing the integration and power management of IoT sensors, optimizing edge deployment, and developing more resilient YOLO models.

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Rajakumari, S.T; Roy, S. (2024). Waste Management: A Telegram — Integrated Smart Garbage Bin. 2024 2nd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), 538–543. https://doi.org/10.1109/ICETEMS64039.2024.10964987.

Dawar, I.; Srivastava, A.; Singal, M.; Dhyani, N.; Rastogi, S. (2025). A systematic literature review on municipal solid waste management using machine learning and deep learning. Artificial Intelligence Review, 58, 183. https://doi.org/10.1007/s10462-025-11196-9.

Nandini Prasad, K.S.; Manjula, G.; Aiswarya, K.J.; Chetankumar; Bhanu Prakash, K.N.; Chandan, V.S. (2024). Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT. 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems, pp. 1788–1793. https://doi.org/10.1109/ICUIS64676.2024.10866251.

Lee, Z.; Wu, Y.C.; Wang, X. (2023). Automated Machine Learning in Waste Classification: A Revolutionary Approach to Efficiency and Accuracy. 2023 12th International Conference on Computing and Pattern Recognition, pp. 299–303. https://doi.org/10.1145/3633637.3633684.

Aswini, G.; Sahana, K; Bhoomika, T.R.; Behera, S.; Thulasi, T.; Desai, U. (2024). IoT Enabled Biomedical Smart Waste Bin Monitoring System using YOLO Algorithm. 2024 IEEE International Conference on Communication, Computing and Signal Processing, pp. 1–5. https://doi.org/10.1109/IICCCS61609.2024.10763584.

Siam, S.I.; et al. (2025). Artificial Intelligence of Things: A Survey. ACM Transactions on Sensor Networks, 21, 1–75. https://doi.org/10.1145/3690639.

Fang, B.; et al. (2023). Artificial intelligence for waste management in smart cities: a review. Environmental Chemistry Letters, 21, 1959–1989. https://doi.org/10.1007/s10311-023-01604-3.

Zhang, J.; Blancaflor, E.B. (2024). A Review of YOLO Algorithm and The Research in Garbage Classification Target Detection. 2024 4th International Joint Conference on Robotics and Artificial Intelligence, pp. 182–187. https://doi.org/10.1145/3696474.3702878.

Wahyutama, A.B.; Hwang, M. (2022). YOLO-Based Object Detection for Separate Collection of Recyclables and Capacity Monitoring of Trash Bins. Electronics, 11, 1323. https://doi.org/10.3390/electronics11091323.

Nancy Deborah, R.; Vidya Janani, V.; Soundarya, M.; Poorvaja, J.V; Alwyn Rajiv. S.; Madhumitha, R. (2024). Waste Segregation using IoT and Machine Learning. 2024 4th International Conference on Advancement in Electronics & Communication Engineering, pp. 5–9. https://doi.org/10.1109/AECE62803.2024.10911797.

Munira, S.; Paul, N.; Alam, M.A.; Rahman, M.M.; Kaiser, M.S. (2024). Turning Trash into Treasure: Developing an Intelligent Bin for Plastic Bottle Recycling. Journal of Social Computing, 5, 1–14. https://doi.org/10.23919/JSC.2024.0001.

Muhamad Itikap, S.; Abdurrahman, M.S.; Soewono, E.B.; Gelar, T. (2023). Geometry and Color Transformation Data Augmentation for YOLOV8 in Beverage Waste Detection. Journal of Software Engineering and Information Communication Technology, 4, 123–138.

Pathak, N.; et al. (2024). Smart City Community Watch—Camera-Based Community Watch for Traffic and Illegal Dumping. Smart Cities, 7, 2232–2257. https://doi.org/10.3390/smartcities7040088.

Reddy, T.A.; Yadukul, S.; Akhilesh, S.; Singh, R.P. (2024). Waste Bottle Detection by Autonomous Surface Vehicles in Inland Water Bodies Using Light YOLOv9 Model. 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, pp. 1–7. https://doi.org/10.1109/UPCON62832.2024.10982747.

Rehman, F.; Rehman, M.; Anjum, M.; Hussain, A. (2025). Optimized YOLOV8: An efficient underwater litter detection using deep learning. Ain Shams Engineering Journal, 16, 103227. https://doi.org/10.1016/j.asej.2024.103227.

Zeb, A.; et al. (2019). A Proposed IoT-Enabled Smart Waste Bin Management System and Efficient Route Selection. Journal of Computer Networks and Communications, 2019, 1–9. https://doi.org/10.1155/2019/7043674.

Mishra, A.; Kumar Ray, A. (2020). IoT cloud‐based cyber‐physical system for efficient solid waste management in smart cities: a novel cost function based route optimisation technique for waste collection vehicles using dustbin sensors and real‐time road traffic informatics. IET Cyber-Physical Systems: Theory & Applications, 5, 330–341. https://doi.org/10.1049/iet-cps.2019.0110.

Anh Khoa, T.; et al. (2020). Waste Management System Using IoT-Based Machine Learning in University. Wireless Communications and Mobile Computing, 2020, 1–13. https://doi.org/10.1155/2020/6138637.

Ghatkamble, R.; Parameshachari, B.D; Pareek, P.K. (2022). YOLO Network Based Intelligent Municipal Waste Management in Internet of Things. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology, pp. 1–10. https://doi.org/10.1109/ICERECT56837.2022.10060062.

Fuqaha, S.; Nursetiawan, N. (2025). Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions. Journal of Future Artificial Intelligence Technology, 2, 24–46. https://doi.org/10.62411/faith.3048-3719-85.

Page, M.J.; et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71.

Terven, J.; Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5, 1680–1716. https://doi.org/10.3390/make5040083.

Jiang, T.; Zhong, Y. (2025). ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11. http://arxiv.org/abs/2502.14314.

Gai, W.; Liu, Y.; Zhang, J.; Jing, G. (2021). An improved Tiny YOLOv3 for real-time object detection. Systems Science & Control Engineering, 9, 314–321. https://doi.org/10.1080/21642583.2021.1901156.

Terven, J.; Cordova-Esparza, D.-M.; Romero-Gonzalez, J.-A. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680–1716. https://doi.org/10.3390/make5040083.

Geetha, A.S. (2025). YOLOv4: A Breakthrough in Real-Time Object Detection. https://doi.org/10.48550/arXiv.2502.04161.

Wang, C.-Y.; Bochkovskiy, A.; Liao, H.-Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https://doi.org/10.48550/arXiv.2207.02696.

dos Santos Júnior, E. S.; Paixão, T.; Alvarez, A. B. (2025). Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images. Applied Sciences, 15, 3164. https://doi.org/10.3390/app15063164.

Wang, C.-Y.; Yeh, I.-H.; Liao, H.-Y. M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. https://doi.org/10.48550/arXiv.2402.13616.

Hidayatullah, P.; Syakrani, N.; Sholahuddin, M. R.; Gelar, T.; Tubagus, R. (2025). YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review. http://arxiv.org/abs/2501.13400.

Alif, M.A.R.; Hussain, M. (2025). YOLOv12: A Breakdown of the Key Architectural Features. http://arxiv.org/abs/2502.14740.

Arishi, A. (2025). Real-Time Household Waste Detection and Classification for Sustainable Recycling: A Deep Learning Approach. Sustainability, 17(5), 1902. https://doi.org/10.3390/su17051902.

Lun, Z.; Pan, Y.; Wang, S.; Abbas, Z.; Islam, M.S.; Yin, S. (2023). Skip-YOLO: Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenes. International Journal of Computational Intelligence Systems, 16(1), 139. https://doi.org/10.1007/s44196-023-00314-6.

Xiao, H.; Luo, H. (2024). Enhancing the adaptability of YOLOv8 for waste detection. Proceedings of the 4th Asia-Pacific Artificial Intelligence and Big Data Forum, New York, pp. 670–674. https://doi.org/10.1145/3718491.3718599.

Liu, S.; Chen, R.; Ye, M.; Luo, J.; Yang, D.; Dai, M. (2024). EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes. Sensors, 24(14), 4666. https://doi.org/10.3390/s24144666.

G, A.; K, S.; Bhoomika, T.R.; Behera, S.; Thulasi, T.; Desai, U. (2024). IoT Enabled Biomedical Smart Waste Bin Monitoring System using YOLO Algorithm. 2024 IEEE International Conference on Communication, Computing and Signal Processing, pp. 1–5. https://doi.org/10.1109/IICCCS61609.2024.10763584.

Wu, W.-K.; Chen, C.-Y.; Lee, J.-S. (2021). Embedded YOLO. Proceedings of the 2021 International Conference on Multimedia Retrieval, New York, pp. 560–565. https://doi.org/10.1145/3460426.3463660.

Isalm, M.R. et al. (2025). UGV-NBWASTE: An oriented dataset for non-biodegradable waste in Bangladesh. Data in Brief, 60, 111559. https://doi.org/10.1016/j.dib.2025.111559.

Sruthi, S.; Shreya Shree, S.; Katariya, P.P.; Singh, R.P. (2025). Small Waste Detection on Land Using fine-tuned YOLOv9 in Unmanned Aerial Vehicle Images. Procedia Computer Science, 258, 2258–2267. https://doi.org/10.1016/j.procs.2025.04.478.

Fan, J.; Cui, L.; Fei, S. (2023). Waste Detection System Based on Data Augmentation and YOLO_EC. Sensors, 23(7), 3646. https://doi.org/10.3390/s23073646.

Yasiri, J.R.; Prathivi, R.; Susanto, S. (2025). Detection of Plastic Bottle Waste Using YOLO Version 5 Algorithm. Sinkron, 9(1), 20–30. https://doi.org/10.33395/sinkron.v9i1.14242.

Huang, C.; Huang, H.; Zhao, P. (2024). A Household Waste Classification System Based on Edge Computing and YOLOv7-T Algorithm. 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), pp. 267–271. https://doi.org/10.1109/ICPICS62053.2024.10796537.

Mustapha, A.A.; Saruchi, S.‘A.; Supriyono, H.; Solihin, M.I. (2025). A Hybrid Deep Learning Model for Waste Detection and Classification Utilizing YOLOv8 and CNN. The 8th Mechanical Engineering, Science and Technology International Conference, Basel, p. 82. https://doi.org/10.3390/engproc2025084082.

Yang, G. et al. (2021). Garbage Classification System with YOLOV5 Based on Image Recognition. 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), pp. 11–18. https://doi.org/10.1109/ICSIP52628.2021.9688725.

Cai, X.; Shuang, F.; Sun, X.; Duan, Y.; Cheng, G. (2022). Towards Lightweight Neural Networks for Garbage Object Detection. Sensors, 22(19), 1–17. https://doi.org/10.3390/s22197455.

Ren, Y.; Li, Y.; Gao, X. (2024). An MRS-YOLO Model for High-Precision Waste Detection and Classification. Sensors, 24(13), 4339. https://doi.org/10.3390/s24134339.

Bianco, S.; Gaviraghi, E.; Schettini, R. (2024). Efficient Deep Learning Models for Litter Detection in the Wild. 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), pp. 601–606. https://doi.org/10.1109/RTSI61910.2024.10761805.

Alharbi, E.; Alsulami, G.; Aljohani, S.; Alharbi, W.; Albaradei, S. (2025). Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment. Scientific Reports, 15(1), 3000. https://doi.org/10.1038/s41598-024-77118-x.

Zhao, F.; Liu, F.; Wang, J.; Chen, Y.; Xi, D.; Shao, X.; Tabeta, S.; Mizuno, K. (2024). Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network. Marine Pollution Bulletin, 209, 117030. https://doi.org/10.1016/j.marpolbul.2024.117030.

Rathod, V.V.; Rana, D.P.; Mehta, R.G. (2025). Unmanned Aerial Vehicle-Based Real-time Garbage Detection System. Procedia Computer Science, 258, 2435–2443. https://doi.org/10.1016/j.procs.2025.04.506.

Shen, X.; Wu, Y.; Chen, S.; Luo, X. (2021). An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles. Mobile Information Systems, 2021, 1–12. https://doi.org/10.1155/2021/5231092.

Madhini, B.; Supraja, P. (2024). Analysis of using Zero Shot Open Vocabulary Detection Methods for Plastic Waste Classification. 2024 5th International Conference on Communication, Computing & Industry 6.0 (C2I6), pp. 1–9. https://doi.org/10.1109/C2I663243.2024.10894834.

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SUBMITTED: 29 May 2025
ACCEPTED: 25 July 2025
PUBLISHED: 4 August 2025
SUBMITTED to ACCEPTED: 57 days
DOI: https://doi.org/10.53623/gisa.v5i2.706

Cite this article
Gelar, T., Fitriani, S., & Rachmat, S. (2025). A Systematic Literature Review of YOLO and IoT Applications in Smart Waste Management. Green Intelligent Systems and Applications, 5(2), 123–139. https://doi.org/10.53623/gisa.v5i2.706
Keywords
IoT Object Detection SLR Waste Management YOLO
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