Automated Waste Classification for Sustainable Cities Using YOLO Based CNN Integrated IoT
Abstract
Sustainable waste management is a vital component of smart city development, directly impacting environmental quality and recycling efficiency. This study presents an IoT-enabled waste classification system that utilizes a Convolutional Neural Network (CNN) for accurate, real-time identification of organic and non-organic waste. The model, implemented using the YOLO architecture, was trained on a diverse dataset of waste images captured under various environmental conditions to ensure robustness in practical scenarios. Classification results are automatically stored in a MySQL database and visualized via an Internet of Things (IoT) based Node-RED dashboard, enabling municipal operators to monitor waste categories and quantities remotely. Field evaluations demonstrate that the system achieves an accuracy of 94%, precision of 94.5%, recall of 93.2%, and an F1-score of 93.85%, indicating high detection reliability and consistent performance, even in challenging urban environments. By integrating CNN-based deep learning with IoT visualization tools, this approach offers a scalable and efficient solution that supports sustainable waste management initiatives within smart city frameworks.
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References
S. Viscardi and C. Colicchia, “A classification of food products to enhance circular economy and reduce waste: A systematic literature review,” Nov. 01, 2024, Elsevier Inc. doi: https://doi.org/10.1016/j.rcradv.2024.200229.
M. A. Budihardjo, N. G. Humaira, B. S. Ramadan, I. F. S. Wahyuningrum, and H. S. Huboyo, “Strategies to reduce greenhouse gas emissions from municipal solid waste management in Indonesia: The case of Semarang City,” Alexandria Engineering Journal, vol. 69, pp. 771–783, Apr. 2023, doi: https://doi.org/10.1016/j.aej.2023.02.029.
Q. Huang, T. Zhao, M. Lin, W. Li, Q. Zhou, and C. Chen, “Insights and practices for industrial solid waste management under ‘zero-waste city’ construction in China,” Sep. 01, 2025, Elsevier B.V. doi: https://doi.org/10.1016/j.cec.2025.100155.
Y. Ren, Y. Li, and X. Gao, “An MRS-YOLO Model for High-Precision Waste Detection and Classification,” Sensors, vol. 24, no. 13, Jul. 2024, doi: https://doi.org/10.3390/s24134339.
W. Nugroho, R. Zahabiyah, M. J. F. Arifiant, and A. Afianto, “Automated Component Detection for Quality PCB Using YOLO Algorithm with IoT Real-Time Streaming on Raspberry Pi,” JURNAL INFOTEL, vol. 17, no. 2, Jul. 2025, doi: https://doi.org/10.20895/infotel.v17i2.1313.
W. Nugroho, R. R. Isnanto, and A. F. Rochim, “Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN,” Jurnal RESTI, vol. 7, no. 1, pp. 80–86, Feb. 2023, doi: https://doi.org/10.29207/resti.v7i1.4626.
M. Naznine et al., “PLDs-CNN-ridge-ELM: Interpretable lightweight waste classification framework,” Eng Appl Artif Intell, vol. 162, Dec. 2025, doi: https://doi.org/10.1016/j.engappai.2025.112522.
V. Moya, M. Guerra, K. Pazmiño, F. Abedrabbo, F. A. Chicaiza, and D. Pozo-Espín, “Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment,” Smart Agricultural Technology, vol. 12, Dec. 2025, doi: https://doi.org/10.1016/j.atech.2025.101221.
W. Nugroho, Rifdah Zahabiyah, Afianto, and Mada Jimmy Fonda Arifianto, “Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection,” Jurnal E-Komtek (Elektro-Komputer-Teknik), vol. 8, no. 2, pp. 428–437, Dec. 2024, doi: https://doi.org/10.37339/e-komtek.v8i2.2187.
D. Vieri, R. Susanto, E. S. Purwanto, and M. K. Ario, “Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 889–895. doi: https://doi.org/10.1016/j.procs.2024.10.316.
M. Nahiduzzaman et al., “An automated waste classification system using deep learning techniques: Toward efficient waste recycling and environmental sustainability,” Knowl Based Syst, vol. 310, Feb. 2025, doi: https://doi.org/10.1016/j.knosys.2025.113028.
S. Sivashanmugam, S. Rodriguez, F. Pour Rahimian, F. Elghaish, and N. Dawood, “Enhancing information standards for automated construction waste quantification and classification,” Aug. 01, 2023, Elsevier B.V. doi: https://doi.org/10.1016/j.autcon.2023.104898.
H. T. N. Le and H. Q. T. Ngo, “Application of the vision-based deep learning technique for waste classification using the robotic manipulation system,” International Journal of Cognitive Computing in Engineering, vol. 6, pp. 391–400, Dec. 2025, doi: https://doi.org/10.1016/j.ijcce.2025.02.005.
S. Immaculate Joy, G. Sriram, and S. Sriram Venkatesan, “Deep CNN-Based Multi-Grade Brain Tumor Classification with Enhanced Data Augmentation,” in Procedia Computer Science, Elsevier B.V., 2025, pp. 300–307. doi: https://doi.org/10.1016/j.procs.2025.03.205.
N. R. Freitas et al., “Multiple mask and boundary scoring R-CNN with cGAN data augmentation for bladder tumor segmentation in WLC videos,” Artif Intell Med, vol. 147, Jan. 2024, doi: 10.1016/j.artmed.2023.102723.
J. S. Kim et al., “Smart microfluidic device for automated exosome quantification and classification using deep learning,” Sens Actuators B Chem, vol. 440, Oct. 2025, doi: https://doi.org/10.1016/j.snb.2025.137919.
A. Z. Viana, C. Månbro, M. Jooshaki, and M. Parian, “Automated ore texture classification using µ-XRF imaging and unsupervised machine learning: Correlation with surface hardness,” Miner Eng, vol. 234, Dec. 2025, doi: https://doi.org/10.1016/j.mineng.2025.109744.
J. Wang, “Application research of image classification algorithm based on deep learning in household garbage sorting,” Heliyon, vol. 10, no. 9, May 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e29966.
Z. Farshadfar, S. H. Khajavi, T. Mucha, and K. Tanskanen, “Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study,” Waste Management, vol. 194, pp. 77–87, Feb. 2025, doi: https://doi.org/10.1016/j.wasman.2025.01.008.
A. F. Hussein, W. M. Hamanah, and M. A. Abido, “Harnessing hyperspectral imaging and deep learning for advanced e-waste classification using three spectral bands,” Results in Engineering, vol. 27, Sep. 2025, doi: https://doi.org/10.1016/j.rineng.2025.106110.
N. S. Ouf, “Leguminous seeds detection based on convolutional neural networks: Comparison of Faster R-CNN and YOLOv4 on a small custom dataset,” Artificial Intelligence in Agriculture, vol. 8, pp. 30–45, Jun. 2023, doi: https://doi.org/10.1016/j.aiia.2023.03.002.
S. Khanal, S. Baral, and J. Jeon, “Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets,” Nuclear Engineering and Technology, vol. 57, no. 10, Oct. 2025, doi: https://doi.org/10.1016/j.net.2025.103703.
R. Kurte, Z. Salcic, and K. I. K. Wang, “Supporting constrained devices and networks in the decentralised Internet of Things,” Internet of Things (The Netherlands), vol. 30, Mar. 2025, doi: https://doi.org/10.1016/j.iot.2025.101496.
C. Y. Chen, S. H. Wu, B. W. Huang, C. H. Huang, and C. F. Yang, “Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform,” Internet of Things (Netherlands), vol. 27, Oct. 2024, doi: https://doi.org/10.1016/j.iot.2024.101305.
K. M. Hosny, W. M. El-Hady, and F. M. Samy, “Technologies, Protocols, and applications of Internet of Things in greenhouse Farming: A survey of recent advances,” Mar. 01, 2025, China Agricultural University. doi: https://doi.org/10.1016/j.inpa.2024.04.002.
R. Casadei, F. Fornari, S. Mariani, and C. Savaglio, “Programming IoT systems: A focused conceptual framework and survey of approaches,” May 01, 2025, Elsevier B.V. doi: https://doi.org/10.1016/j.iot.2025.101548.
A. S. M. Mohsin, S. H. Choudhury, and M. A. Muyeed, “Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML),” Transportation Engineering, vol. 19, Mar. 2025, doi: https://doi.org/10.1016/j.treng.2025.100304.
S. Hume, G. West, and G. Dobie, “A framework for capturing and representing the process to classify nuclear waste and informing where processes can be automated,” Progress in Nuclear Energy, vol. 170, May 2024, doi: https://doi.org/10.1016/j.pnucene.2024.105133.
A. Gatti, E. Barbierato, and A. Pozzi, “Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection,” Mar. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: https://doi.org/10.3390/electronics13050836.
A. Bisciotti, V. Brombin, Y. Song, G. Bianchini, and G. Cruciani, “Classification and predictive leaching risk assessment of construction and demolition waste using multivariate statistical and machine learning analyses,” Waste Management, vol. 196, pp. 60–70, Apr. 2025, doi: https://doi.org/10.1016/j.wasman.2025.02.033.
