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All Journal J-Sil (Jurnal Teknik Sipil dan Lingkungan) Torani Journal of Fisheries and Marine Science JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) Jurnal Sains & Teknologi Lingkungan Chemistry Indonesian Journal of Chemical Research Equilibrium Journal of Chemical Engineering Journal of Applied Data Sciences Cerdika: Jurnal Ilmiah Indonesia J-Sil (Jurnal Teknik Sipil dan Lingkungan)
Shabrina, Husna Muizzati
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Public Health Social Sciences Transportation

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YOLOv8-Based Microplastic Detection and Quantification in River Water Microscopic Images Pradana, Musthofa Galih; Nyamiati, Retno Dwi; Shabrina, Husna Muizzati; Adrezo, Muhammad; Maulana, Nurhuda
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 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.
Co-Authors Adhina, Ufiya Cahaya Afifah, Nuha Amiratul Agus Adhiatma Andri Saputra Aningtyas, Vebri Barti Setiani Muntalif Barti Setiani Muntalif Faradilla, Febrica Citra Fauzan Irfandy Heni Anggorowati Inat Shani Fathuna Indriana Lestari Mayrina Firdayati Muhammad Adrezo Muhammad Amin Musthofa Galih Pradana Nurhuda Maulana Nyamiati, Retno Dwi Perwitasari Perwitasari Perwitasari Perwitasari Pratiwi, Distika Prayogo, Wisnu Ratnaningsih, Wahyu Sobirin, Ahmad Uli Ulfa Uma Fadzilia Arifin, Uma Fadzilia Yudhistira Saraswati