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A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
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Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
A new yet little understood threat to our ecosystems is microplastics.
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This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.
Tall Trees and Small Plastics. Using Random Forest Classification to Identify Microplastic Pollution in Surface Soil Samples
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Researchers used machine learning (random forest classification) to identify and distinguish twenty types of plastic particles in soil samples from agricultural land. Developing accurate, automated detection methods for microplastics in soil is essential for large-scale environmental monitoring.
Detection of Microplastics Using Machine Learning
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Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
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This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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Researchers tested the robustness of decision tree and random forest machine learning classifiers combined with Nile red fluorescent staining for automated detection and identification of microplastic polymers weathered under semi-controlled surface water and deep-sea conditions for up to one year. They found both models achieved comparable accuracy above 90% for pristine plastics, but assessed how environmental weathering affected classification reliability, also evaluating analysis time, model complexity, size detection limits, and interoperability.
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