Deeplearning Based Mobile App for Image Capture and Microplastic Analysis in Water Systems

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

FasterRes-FPN: A Deep Learning Approach for Microplastic Pollution Surveillance in Water Bodies

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Researchers developed FasterRes-FPN, a deep learning model for automated microplastic detection in water bodies, offering a faster and more cost-effective alternative to traditional microscopy and spectroscopy methods for large-scale aquatic pollution surveillance.

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.

Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning

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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.

Microplastic Detection and Analysis in Water Samples

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Thermoanalytical and spectroscopic methods enable detection and quantification of microplastics in water samples, which is critical for tracing pollution sources and protecting drinking water and desalination infrastructure. Robust water monitoring methods are essential for assessing human exposure risks from microplastic-contaminated water supplies.