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Developing and evaluating automated deep learning and human-in-the-loop vision–language systems for microplastic characterization
Summary
Scientists built two AI tools to automatically identify and sort microplastic particles from microscope images, one trained specifically for the task, and one that's a more flexible general-purpose AI assisted by human experts. The specialized AI performed best overall, but the human-assisted general AI wasn't far behind and offered more transparency for tricky cases, making it easier for regular labs to run large-scale microplastic testing without needing a fully custom-built system. Faster, cheaper microplastic monitoring like this matters because it could help researchers track where these tiny pollutants show up in our water, food, and environment, an important step toward understanding their pot
Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) classifier based on EfficientNet-B0 with transfer learning, trained on an in-house dataset of approximately 700 annotated microscope images to simultaneously predict microplastic shape/type (five classes), color (10 classes), and surface texture (two classes). The second paradigm employs the Claude Vision API as a zero-shot vision-language model (VLM), augmented with a structured human-in-the-loop (HITL) mechanism allowing domain experts to provide targeted guidance for ambiguous particles. Both systems were evaluated on an identical, independent test set using accuracy, macro-averaged precision, recall, and F1-score. The DL classifier achieved F1-scores of 91.2%, 88.5%, and 85.1% for shape/type, color, and texture classification, respectively. In contrast, the VLM achieved raw F1-scores ranging between 72 and 81% across the evaluated tasks, which improved substantially to approximately 84-89% following expert-guided refinement. These results demonstrate that the trained DL model excels in high-throughput, reproducible screening, while the VLM-HITL system offers enhanced interpretability and flexibility for ambiguous cases. This comparative framework, deployed as a freely accessible web application via Hugging Face Spaces, provides practical insights into the deployment trade-offs between domain-specific and generalist AI approaches for environmental microplastic analysis.