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Application of Artificial Intelligence to Improve Microplastic Identification
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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
The pervasiveness of microplastics in surroundings is required for the identification and measurement as it poses a complex problem. Sampling, separation, detection, and characterization of microplastics are complex and challenging. The conventional methods of...
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