Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

Jutarut Chaoraingern, Vittaya Tipsuwanporn, Arjin Numsomran


BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.


Artificial intelligence; deep learning; embedded AI; plastic waste classification; Tiny ML

Full Text:



R. Kumar, A. Verma, A. Shome, R. Sinha, S. Sinha, P. Jha, R. Kumar, P. Kumar, Shubham, S. Das, P. Sharma, and P. Prasad, "Impacts of plastic pollution on ecosystem services, sustainable development goals, and need to focus on circular economy and policy interventions," Sustainability, vol. 13, no. 17, p. 9963, Sep. 2021, doi: 10.3390/su13179963.

M. Edelson, D. HÃ¥besland, and R. Traldi, "Uncertainties in global estimates of plastic waste highlight the need for monitoring frameworks," Marine Pollution Bulletin, vol. 171, p. 112720, Oct. 2021, doi: 10.1016/j.marpolbul.2021.112720.

L. Tang, J. C. Feng, C. Li, J. Liang, S. Zhang, and Z. Yang, "Global occurrence, drivers, and environmental risks of microplastics in marine environments," Journal of Environmental Management, vol. 329, p. 116961, Mar. 2023, doi: 10.1016/j.jenvman.2022.116961.

H. Ritchie and M. Roser, "Plastic pollution," Our World in Data.

D. Marks, M. A. Miller, and S. Vassanadumrongdee, "The geopolitical economy of Thailand's marine plastic pollution crisis," Asia Pacific Viewpoint, vol. 61, no. 2, pp. 266–282, Jan. 2020, doi: 10.1111/apv.12255.

Thailand investment review, Thailand's bio-circular-green economy: living up to global challenges, Thailand Board of Investment, Vol. 21, 2121.

Thailand state of pollution 2020 (B.E. 2563), Ministry of Natural Resources and Environment, Pollution Control Department, Vol. 26, 2021.

Closing the loop on plastic pollution in nakhon si thammarat, Thailand, baseline report, The United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), 2021.

E. R. K. Neo, Z. Yeo, J. S. C. Low, V. Goodship, and K. Debattista, "A review on chemometric techniques with infrared, Raman and laser-induced breakdown spectroscopy for sorting plastic waste in the recycling industry," Resources, Conservation and Recycling, vol. 180, p. 106217, May 2022, doi: 10.1016/j.resconrec.2022.106217.

U. K. Adarsh, E. Bhoje Gowd, A. Bankapur, V. B. Kartha, S. Chidangil, and V. K. Unnikrishnan, "Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management," Waste Management, vol. 150, pp. 339–351, Aug. 2022, doi: 10.1016/j.wasman.2022.07.025.

F. K. Konstantinidis, S. Sifnaios, G. Tsimiklis, S. G. Mouroutsos, A. Amditis, and A. Gasteratos, "Multi-sensor cyber-physical sorting system (CPSS) based on Industry 4.0 principles: A multi-functional approach," Procedia Computer Science, vol. 217, pp. 227–237, 2023, doi: 10.1016/j.procs.2022.12.218.

S. Majchrowsk, S. Majchrowska, A. Mikołajczyk, M. Ferlin, Z. Klawikowska, M. A. Plantykow, A. Kwasigroch, and K. Majek, "Deep learning-based waste detection in natural and urban environments," Waste Management, vol. 138, pp. 274–284, Feb. 2022, doi: 10.1016/j.wasman.2021.12.001.

Z. Chen, J. Yang, L. Chen, and H. Jiao, "Garbage classification system based on improved ShuffleNet v2," Resources, Conservation and Recycling, vol. 178, p. 106090, Mar. 2022, doi: 10.1016/j.resconrec.2021.106090.

T. J. Sheng, M. S. Islam, N. Misran, M. H. Baharuddin, H. Arshad, M. R. Islam, M. E. H. Chowdhury, H. Rmili, and M. T. Islam , "An internet of things based smart waste management system using LoRa and Tensorflow deep learning model," IEEE Access, vol. 8, pp. 148793–148811, 2020, doi: 10.1109/access.2020.3016255.

N. C. A. Sallang, M. T. Islam, M. S. Islam, and H. Arshad, "A CNN-based smart waste management system using TensorFlow Lite and LoRa-GPS shield in internet of things environment," IEEE Access, vol. 9, pp. 153560–153574, 2021, doi: 10.1109/access.2021.3128314.

T. W. Wu, H. Zhang, W. Peng, F. Lü, and P.-J. He, "Applications of convolutional neural networks for intelligent waste identification and recycling: A review," Resources, Conservation and Recycling, vol. 190, p. 106813, Mar. 2023, doi: 10.1016/j.resconrec.2022.106813.

Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu, "Waste image classification based on transfer learning and convolutional neural network," Waste Management, vol. 135, pp. 150–157, Nov. 2021, doi: 10.1016/j.wasman.2021.08.038.

W. L. Mao, W. C. Chen, C. T. Wang, and Y. H. Lin, "Recycling waste classification using optimized convolutional neural network," Resources, Conservation and Recycling, vol. 164, p. 105132, Jan. 2021, doi: 10.1016/j.resconrec.2020.105132.

K. Lin, T. Zhou, X. Gao, Z. Li, H. Duan, H. Wu, G. Lu, Y. Zhao, "Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer," Journal of Environmental Management, vol. 318, p. 115501, Sep. 2022, doi: 10.1016/j.jenvman.2022.115501.

S. Jin, Z. Yang, G. Królczykg, X. Liu, P. Gardoni, and Z. Li, "Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling," Waste Management, vol. 162, pp. 123–130, May 2023, doi: 10.1016/j.wasman.2023.02.014.

Y. Zhao, H. Huang, Z. Li, H. Yiwang, and M. Lu, "Intelligent garbage classification system based on improve MobileNetV3-Large," Connection Science, vol. 34, pp. 1299-1321, Apr. 2022, doi: 10.1080/09540091.2022.2067127.

Z. Shelby, "The data driven engineering revolution," Embedded Vision Summit, 2021.

C. Sager, C. Janiesch, and P. Zschech, "A survey of image labelling for computer vision applications," Journal of Business Analytics, vol. 4, no. 2, pp. 91–110, Apr. 2021, doi: 10.1080/2573234x.2021.1908861.

T. Yang, X. Yu, N. Ma, Y. Zhang, and H. Li, "Deep representation-based transfer learning for deep neural networks," Knowledge-Based Systems, vol. 253, p. 109526, Oct. 2022, doi: 10.1016/j.knosys.2022.109526.

S. B. Thunuguntla, S. Murugaanandam, and R. Pitchai, "Densenet121-DNN-based hybrid approach for advertisement classification and user identification," International Journal of Intelligent Engineering and Systems, vol. 16, no. 3, pp. 162–174, Jun. 2023, doi: 10.22266/ijies2023.0630.13.

S. Hartini, Z. Rustam, and R. Hidayat, "Designing hybrid CNN-SVM model for COVID-19 classification based on X-ray images using LGBM feature selection," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 5, p. 1895, Sep. 2022, doi: 10.18517/ijaseit.12.5.16875.

P. P. Ray, "A review on TinyML: state-of-the-art and prospects," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1595–1623, Apr. 2022, doi: 10.1016/j.jksuci.2021.11.019.

K. Filus and J. Domańska, "Software vulnerabilities in TensorFlow-based deep learning applications," Computers & Security, vol. 124, p. 102948, Jan. 2023, doi: 10.1016/j.cose.2022.102948.

S. Hymel, "Edge Impulse: an MLOps platform for Tiny Machine Learning,", Nov. 02, 2022.

Hymel, S., Banbury, C., Situnayake, D., Elium, A., Ward, C., Kelcey, M., Baaijens, M., Majchrzycki, M., Plunkett, J., Tischler, D., Grande, A., Moreau, L., Maslov, D., Beavis, A., Jongboom, J., & Reddi, V. J. (2022, November 2). Edge Impulse: an MLOps platform for Tiny Machine Learning.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development