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Beta Testing an AI-Based Physical Analysis Technology for Microplastic Quantification and Characterization
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Researchers beta-tested a portable AI imaging system for counting and characterizing microplastics, examining fragments, pellets, and films in both clean and weathered conditions. The prototype achieved 89% detection accuracy on clean samples, demonstrating potential as a field-deployable microplastic quantification tool.
Microplastic pollution is accumulating at alarming rates in the natural environment. New and innovative technologies are needed to help understand the gravity of the global microplastic pollution. In this study, a portable artificial intelligence system using image capture and analysis technology was beta tested to determine its suitability for microplastic quantification and characterization. Many factors were examined, including quantity, colour, shape and appearance (i.e., fragment, pellet, and film), and environmentally simulated (i.e., weathered and humic acid soaked). These were all factors considered. The beta prototype showed a pronounced aptitude for microplastic detection with a clean microplastic detection accuracy of 89% and an environmentally simulated microplastic detection accuracy of 77%. The beta prototype was compact, easy to use, and provided extensive information about the samples through its machine learning algorithm. The beta prototype would be well-suited for both scientific research and citizen science and is ideal for larger (≥0.5 mm) and lighter-coloured microplastic characterization.
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