YOLOv8-Based Microplastic Detection and Quantification in River Water Microscopic Images

Musthofa Galih Pradana, Retno Dwi Nyamiati, Husna Muizzati Shabrina, Muhammad Adrezo, Nurhuda Maulana
Pages 1533-1544
Abstract Views 488
PDF Downloads 502
Crossref Citations -
DOI https://doi.org/10.47738/jads.v7i2.1262

Abstract


Plastic particles with various size variations such as microplastics are environmental contaminants that are widely found in waters and have the potential to cause negative impacts. The process of identifying plastic particles using microscopic imagery manually takes a lot of time and considerable cost. In order to provide an alternative solution as part of early detection, microscopic image-based plastic particle detection was carried out with the YOLOv8 architecture, accompanied by an estimate of microplastic abundance in microplastic units per cubic meter. This study aims to develop and evaluate the detection of plastic particles in microscopic images of river water. This research dataset consists of 300 microscopic images taken from three river locations in Indonesia and annotated for model training and testing. The results of the evaluation showed that the proposed model had an aggregate performance value with a precision value of 0.786, recall of 0.66, and mAP@0.5 of 0.731. Additional test results show that with the addition of image resolution, the precision value can increase to 0.804 and the value mAP@0.5 increases to 0.762, even at the expense of computing time, which is also increasing. Extended scenario-based analysis showed that more than 87% of the detected objects fell into the category of small objects, affecting the localization sensitivity and variability of the estimated MPS value. This study also validated the results of object detection with FTIR-based laboratory tests using a full quantitative agreement between the model detection results and the identification of plastic particle materials at the sampling location level. The main contribution and findings of this study is an integrated evaluation framework for object detection, particle size characterization which is expected to be an alternative to the initial screening tool for plastic particle content.


Keywords


Microplastics; YOLOv8; Object Detection; Microscopic Images; FTIR Validation

Full Text:

PDF

References


C. E. Syl Kacapyr, “Study maps human uptake of microplastics across 109 countries,” https://news.cornell.edu/stories/2024/05/study-maps-human-uptake-microplastics-across-109-countries.

Statista, “Annual production of plastics worldwide from 1950 to 2023.”

Y. Luo, O. S. Awoyemi, R. Naidu, and C. Fang, “Detection of microplastics and nanoplastics released from a kitchen blender using Raman imaging,” J. Hazard. Mater., vol. 453, p. 131403, Jul. 2023, doi: 10.1016/j.jhazmat.2023.131403.

A. Tarafdar, S.-H. Choi, and J.-H. Kwon, “Differential staining lowers the false positive detection in a novel volumetric measurement technique of microplastics,” J. Hazard. Mater., vol. 432, p. 128755, Jun. 2022, doi: 10.1016/j.jhazmat.2022.128755.

A. Priyanto, D. A. Hapidin, D. Edikresnha, M. P. Aji, and K. Khairurrijal, “Predicting microplastic quantities in Indonesian provincial rivers using machine learning models,” Science of The Total Environment, vol. 961, p. 178411, Jan. 2025, doi: 10.1016/j.scitotenv.2025.178411.

L. Lv et al., “Challenge for the detection of microplastics in the environment,” Water Environment Research, vol. 93, no. 1, pp. 5–15, Jan. 2021, doi: 10.1002/wer.1281.

A. Nene et al., “Recent advances and future technologies in nano-microplastics detection,” Environ. Sci. Eur., vol. 37, no. 1, p. 7, Jan. 2025, doi: 10.1186/s12302-024-01044-y.

P. Guo, Y. Wang, P. Moghaddamfard, W. Meng, S. Wu, and Y. Bao, “Artificial intelligence-empowered collection and characterization of microplastics: A review,” J. Hazard. Mater., vol. 471, p. 134405, Jun. 2024, doi: 10.1016/j.jhazmat.2024.134405.

H. Jin, F. Kong, X. Li, and J. Shen, “Artificial intelligence in microplastic detection and pollution control,” Environ. Res., vol. 262, p. 119812, Dec. 2024, doi: 10.1016/j.envres.2024.119812.

D. Arisandi, A. Khoerunnisa, R. Subekti, A. Eko Setiawan, and C. Ramdani, “Performance Evaluation of CLAHE-Enhanced Edge Detection on Low-Light Faces,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 1, pp. 7–10, Jul. 2025, doi: 10.64472/jciet.v1i1.2.

B. Hu et al., “Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors,” J. Hazard. Mater., vol. 474, p. 134865, Aug. 2024, doi: 10.1016/j.jhazmat.2024.134865.

M. G. Pradana, H. Khoirunnisa, and I. W. R. Pinastawa, “Evaluation of Convolutional Neural Network Model Architecture Performance,” pp. 628–632, 2023, doi: 10.1109/icimcis60089.2023.10349075.

D. R. Maulana, M. G. Pradana, and M. P. Muslim, “Handwriting Classification of Sundanese Script Using LBP Feature Extraction and CNN,” in 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), IEEE, Nov. 2024, pp. 1079–1084. doi: 10.1109/ICIMCIS63449.2024.10956206.

M. D. A. Hasan, K. Balasubadra, G. Vadivel, N. Arunfred, M. V. Ishwarya, and S. Murugan, “IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks,” in 2024 2nd International Conference on Computer, Communication and Control (IC4), IEEE, Feb. 2024, pp. 1–6. doi: 10.1109/IC457434.2024.10486490.

M. Giardino, V. Balestra, D. Janner, and R. Bellopede, “Automated method for routine microplastic detection and quantification,” Science of The Total Environment, vol. 859, p. 160036, Feb. 2023, doi: 10.1016/j.scitotenv.2022.160036.

K. Liu, X. Pang, H. Chen, and L. Jiang, “Visual detection of microplastics using Raman spectroscopic imaging,” Analyst, vol. 149, no. 1, pp. 161–168, 2024, doi: 10.1039/D3AN01270K.

D. Ho and H. Feng, “Shedding Light on the Polymer’s Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP),” Feb. 2025, doi: 10.1016/j.jece.2025.117944.

J. Lorenzo-Navarro et al., “Deep learning approach for automatic microplastics counting and classification,” Science of The Total Environment, vol. 765, p. 142728, Apr. 2021, doi: 10.1016/j.scitotenv.2020.142728.

H. Park et al., “MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams,” PLoS One, vol. 17, no. 6, p. e0269449, Jun. 2022, doi: 10.1371/journal.pone.0269449.

X.-L. Han, N.-J. Jiang, T. Hata, J. Choi, Y.-J. Du, and Y.-J. Wang, “Deep learning based approach for automated characterization of large marine microplastic particles,” Mar. Environ. Res., vol. 183, p. 105829, Jan. 2023, doi: 10.1016/j.marenvres.2022.105829.

M. A. B. Sarker, M. H. Imtiaz, T. M. Holsen, and A. B. M. Baki, “Real-Time Detection of Microplastics Using an AI Camera,” Sensors, vol. 24, no. 13, p. 4394, Jul. 2024, doi: 10.3390/s24134394.

T. Thammasanya, S. Patiam, E. Rodcharoen, and P. Chotikarn, “A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light,” Sci. Rep., vol. 14, no. 1, p. 3529, Feb. 2024, doi: 10.1038/s41598-024-53251-5.

S.-J. Royer, H. Wolter, A. E. Delorme, L. Lebreton, and O. B. Poirion, “Computer vision segmentation model deep learning for categorizing microplastic debris,” Front. Environ. Sci., vol. 12, Jul. 2024, doi: 10.3389/fenvs.2024.1386292.

C. Liu et al., “Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics,” Toxicology, vol. 508, p. 153918, Nov. 2024, doi: 10.1016/j.tox.2024.153918.

S. Qian, X. Qiao, W. Zhang, Z. Yu, S. Dong, and J. Feng, “Machine learning-based prediction for settling velocity of microplastics with various shapes,” Water Res., vol. 249, p. 121001, Feb. 2024, doi: 10.1016/j.watres.2023.121001.

Y. Zhen, L. Wang, H. Sun, and C. Liu, “Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning,” Environmental Pollution, vol. 331, p. 121834, Aug. 2023, doi: 10.1016/j.envpol.2023.121834.

J. Li, Z. Jiang, L. Shu, X. Li, C. Wang, and H. Zhang, “Machine learning models for forecasting microplastic dynamics in China’s coastal waters,” J. Hazard. Mater., vol. 494, p. 138797, Aug. 2025, doi: 10.1016/j.jhazmat.2025.138797.

Y. Qiu, Z. Li, T. Zhang, and P. Zhang, “Predicting aqueous sorption of organic pollutants on microplastics with machine learning,” Water Res., vol. 244, p. 120503, Oct. 2023, doi: 10.1016/j.watres.2023.120503.

X. Jin et al., “Quantitative assessment on the distribution patterns of microplastics in global inland waters,” Commun. Earth Environ., vol. 6, no. 1, p. 331, Apr. 2025, doi: 10.1038/s43247-025-02320-2.

U. Ihezukwu, C. Charoenpong, and S. Chotpantarat, “Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand,” Environmental Pollution, vol. 375, p. 126346, Jun. 2025, doi: 10.1016/j.envpol.2025.126346.

M. A. M. Reshadi et al., “Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning,” Sci. Rep., vol. 15, no. 1, p. 6299, Feb. 2025, doi: 10.1038/s41598-025-90612-0.

H.-T. Tran et al., “Machine learning approaches for predicting microplastic pollution in peatland areas,” Mar. Pollut. Bull., vol. 194, p. 115417, Sep. 2023, doi: 10.1016/j.marpolbul.2023.115417.

A. Z. Fazil, D. D. S. Dhawala Wijeratna, and P. I. A. Gomes, “Predicting microplastic transport in open channels with different bed types and river regulation with machine learning techniques,” Environmental Pollution, vol. 384, p. 126912, Nov. 2025, doi: 10.1016/j.envpol.2025.126912.

R Isaac Sajan, M Manchu, C Felsy, and M Joselin Kavitha, “Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM),” J. Environ. Health Sci. Eng., vol. 22, no. 2, pp. 579–592, Sep. 2024, doi: 10.1007/s40201-024-00920-2.

H. Moussaoui et al., “Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition,” Sci. Rep., vol. 14, no. 1, p. 14389, Jun. 2024, doi: 10.1038/s41598-024-65272-1.

J. Xu, H. Ren, S. Cai, and X. Zhang, “An improved faster R-CNN algorithm for assisted detection of lung nodules,” Comput. Biol. Med., vol. 153, p. 106470, Feb. 2023, doi: 10.1016/j.compbiomed.2022.106470.

E. N. Yilmaz and T. S. Navruz, “Real-Time Object Detection: A Comparative Analysis of YOLO, SSD, and EfficientDet Algorithms,” in 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), IEEE, May 2025, pp. 1–9. doi: 10.1109/ICHORA65333.2025.11017287.

Z. Li, C. Pang, C. Dong, and X. Zeng, “R-YOLOv5: A Lightweight Rotational Object Detection Algorithm for Real-Time Detection of Vehicles in Dense Scenes,” IEEE Access, vol. 11, pp. 61546–61559, 2023, doi: 10.1109/ACCESS.2023.3262601.

R. Varghese and S. M., “YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness,” in 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), IEEE, Apr. 2024, pp. 1–6. doi: 10.1109/ADICS58448.2024.10533619.




DOI: https://doi.org/10.47738/jads.v7i2.1262

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0