Microplastics in the rough: using data augmentation to identify plastics contaminated by water and plant matter

Summary

This study developed machine learning approaches using data augmentation to improve the identification of microplastics in "real world" samples where particles are contaminated by water droplets, soil, or plant material. Accurately classifying weathered and dirty microplastics from spectral images is a practical challenge that limits field research, and the techniques developed here improve detection accuracy. Better identification tools are a necessary step toward reliable monitoring of microplastic pollution across diverse environments.