Speaker
Description
Microplastic (MP) pollution in freshwater systems remains poorly monitored due to the cost and complexity of existing identification methods, which typically rely on high-magnification imaging or laboratory-based spectroscopy. MicroSight addresses this gap by developing a machine learning pipeline for automated MP classification from images captured with consumer-grade devices, enabling scalable, field-deployable monitoring.
The project's first phase focuses on building an open, annotated image dataset of MP particles (fragments, fibers, films, foams, pellets) prepared under controlled laboratory conditions and varied environmental backgrounds, including sediment and water matrices, biofouling, and weathering, to reflect real-world sampling scenarios. Particle segmentation and annotation combine pre-trained foundation models with manual verification, and physically meaningful shape descriptors (aspect ratio, roundness, solidity, convexity) are extracted to support morphology-based classification alongside deep learning features.
We will present the first labelled iteration of this dataset, along with baseline classification results from an initial ML model, including an early assessment of how image resolution affect classification performance. These results lay the groundwork for an open-access platform for crowdsourced MP recognition, offering a reproducible, low-cost framework for large-scale environmental monitoring and a foundation dataset for the broader scientific community.