Edge AI-Driven Microfluidic Platform for Real-Time Detection and Classification of Microplastic Particles in Environmental Samples

Badugu Vimala Victoria, Kamil Reza Khondakar

2025

Abstract

Microplastics have become a major concern in our daily life, affecting the environment and health and as such, requiring advanced detection techniques. Existing methods of microplastics detection are manual and require sophisticated laboratories. This project intends to solve those problems by developing a real-time microplastic detection system based on Edge AI on an NVIDIA Jetson Nano system. Computer Vision is integrated with a new YOLOv5 Object Detection and Gradient Boosting Classifier (GBC) hybrid algorithm which classifies microplastic particles into different classes based on shape, size, color, texture, and shape as well as a classifier combining GBC features. Detection accuracy is further improved with ensemble learning methods such as bagging, boosting and stacking. For deploying the model at the edge, the hybrid model is optimized using Tensor RT quantization which offers real-time analysis for the Jetson Nano. The system is tested for accuracy, precision, recall, and F1 score against manual identification using a microscope. Comprehensive data logging and visualization interface is developed to track microplastic pollution in real time for other environmental health purposes.

Paper Citation

in Harvard Style

Victoria B. and Khondakar K. (2025). Edge AI-Driven Microfluidic Platform for Real-Time Detection and Classification of Microplastic Particles in Environmental Samples. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 652-664. DOI: 10.5220/0013870800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Badugu Victoria and Kamil Khondakar},
title={Edge AI-Driven Microfluidic Platform for Real-Time Detection and Classification of Microplastic Particles in Environmental Samples},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={652-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013870800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Edge AI-Driven Microfluidic Platform for Real-Time Detection and Classification of Microplastic Particles in Environmental Samples
SN - 978-989-758-777-1
AU - Victoria B.
AU - Khondakar K.
PY - 2025
SP - 652
EP - 664
DO - 10.5220/0013870800004919
PB - SciTePress