Development of fine-tuned deep learning models for physical classification of microplastics extracted from different environmental matrices

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning

AI summary · Read the abstract

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.

Laser induced fluorescence and machine learning: a novel approach to microplastic identification

AI summary · Read the abstract

Researchers combined laser-induced fluorescence with machine learning to identify microplastics in water in real time, achieving 97.6% accuracy in distinguishing plastics from other organic matter and 88.3% accuracy in identifying specific plastic types — a promising step toward automated ocean monitoring.

Application of Artificial Intelligence to Improve Microplastic Identification

AI summary · Read the abstract

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.

Defining Irregular Microplastics: A Machine Learning Approach for Morphometric Characterization

AI summary · Read the abstract

Researchers applied a machine learning decision-tree classifier to hyperspectral images of 129 nano- and microplastic particles and identified five morphometric descriptors—circularity, roundness, solidity, long diameter, and perimeter-area ratio—that together distinguish irregular particles from spherical and fibrous forms with 96% accuracy, providing a standardized definition for the most environmentally prevalent but least-defined shape class.