Identification of environmental microplastics using large language models: DeepSeek-R1-Distill-Llama-8B, GPT-4o, and GPT-4o-mini

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

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.

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.

What’s that microplastic? Advances in machine learning are making identifying plastics in the environment more reliable

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This piece reviews advances in machine learning for identifying polymer types in environmental microplastic samples, describing how models trained on spectroscopic and imaging data are improving the reliability and speed of plastic classification. Better automated identification tools are essential for building standardized datasets needed to track pollution sources, assess regulatory compliance, and understand human and ecological exposure to specific plastic types.

A Machine-Learning Model for Investigating Microplastics Source–Receptor Relationships in Aquatic Environments

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Researchers developed a machine-learning model to trace where microplastics in aquatic environments come from and where they end up, potentially giving scientists and regulators a more powerful tool to identify pollution sources and prioritize cleanup efforts.