Advances in machine learning for the detection and characterization of microplastics in the environment

Microplastics are increasingly recognized as a pervasive pollutant in both aquatic and terrestrial environments, raising pressing concerns about their ecological impacts and implications for human health. Traditional detection and quantification methods—including manual microscopy and standalone spectroscopic techniques—offer reliable accuracy but are limited by labor-intensive procedures and low throughput. Recent advances in machine learning (ML) have revolutionized the field of microplastic research by automating and enhancing detection processes. In particular, algorithms such as support vector machines, random forests, and convolutional neural networks have demonstrated considerable success in classifying…