Deep-learning-assisted near-infrared hyperspectral imaging for microplastic classification

Classification of biodegradable microplastics in soil using short-wave infrared hyperspectral imaging and machine learning: a laboratory-based proof-of-concept study

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A machine learning approach using short-wave infrared hyperspectral imaging achieved up to 95.98% accuracy in classifying both biodegradable and conventional microplastics in soil matrices without physical destruction of samples. Rapid, non-destructive detection methods like this are essential for monitoring the growing presence of biodegradable microplastics in agricultural soils as these materials are more widely adopted.

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.

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

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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.

Optical RGB-IR Classification Method for a Drone-Mounted Microplastics Detector: Plastic vs. Metal Discrimination