Edge AI-Driven Microfluidic Platform for Real-Time Detection and Classification of Microplastic Particles in Environmental Samples

AI-driven portable smartphone-based sensor for microplastic detection: a comprehensive review

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Researchers reviewed AI-integrated smartphone sensors for microplastic detection and found that systems combining convolutional neural networks with optical sensing achieve 96–99% accuracy, offering a portable and affordable alternative to laboratory methods that could enable large-scale distributed environmental monitoring.

Rapid and low-cost detection platform for micro- and nanoplastics

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.

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

Microfluidic platforms for detection and biological evaluation of micro- and nanoplastics