AQUAEYE: A STANDARDIZED SYSTEM FOR QUANTITATIVE ANALYSIS OF MICROPARTICULATE MATTER

Automated microplastic quantification is currently compromised by morphological mimicry: air bubbles, organic biofilms, and sediment create high false-positive rates in standard Convolutional Neural Networks (CNNs).This study introduces AquaEye, a containerized computer vision framework that mitigates artifact misclassification by fusing Bayesian Deep Learning with ISO-standard morphometrics.Unlike deterministic U-Net implementations, we deploy a Monte Carlo Dropout inference pipeline to estimate epistemic uncertainty, enabling the suppression of predictions where model variance exceeds a safety threshold.To enforce physical validity, a post-processing geometric gate rejects candidates based on Circularity (4πA/P 2 ) and Solidity, filtering non-polymer structures that bypass the neural filter.The system, deployed via a Dockerized microservices architecture, ensures reproducibil-ity often absent in -lab-bench‖ scripts.Experimental validation confirms that AquaEye statistically decouples true microplastic instances from background noise, offering a robust alternative to manual microscopy for highthroughput environmental monitoring.