Microplastics have recently been discovered as remarkable contaminants of all environmental matrices. Their quantification and characterisation require lengthy and laborious analytical procedures that make this aspect of microplastics research a critical issue. In light of this, in this work, we developed a Computer Vision and Machine-Learning-based system able to count and classify microplastics quickly and automatically in four morphology and size categories, avoiding manual steps. Firstly, an early machine learning algorithm was created to count and classify microplastics. Secondly, a supervised (k-nearest neighbours) and an unsupervised classification were developed to determine microplastic quantities and properties and discover hidden information. The machine learning algorithm showed promising results regarding the counting process and classification in sizes; it needs further improvements in visual class classification. Similarly, the supervised classification demonstrated satisfactory results with accuracy always greater than 0.9. On the other hand, the unsupervised classification discovered the probable underestimation of some microplastic shape categories due to the sampling methodology used, resulting in a useful tool for bringing out non-detectable information by traditional research approaches adopted in microplastic studies. In conclusion, the proposed application offers a reliable automated approach for microplastic quantification based on counts of particles captured in a picture, size distribution, and morphology, with considerable prospects in method standardisation.

中文翻译：

---

基于计算机视觉和机器学习算法对微塑料进行计数和分类的便捷开源应用程序

最近发现微塑料是所有环境基质中的显着污染物。它们的量化和表征需要漫长而费力的分析程序，这使得微塑料研究的这一方面成为一个关键问题。有鉴于此，在这项工作中，我们开发了一个基于计算机视觉和机器学习的系统，能够快速、自动地按四种形态和尺寸类别对微塑料进行计数和分类，避免手动步骤。首先，创建了一种早期的机器学习算法来对微塑料进行计数和分类。其次，开发了有监督（k-最近邻）和无监督分类来确定微塑料的数量和特性并发现隐藏的信息。机器学习算法在计数过程和大小分类方面显示出有希望的结果；它需要进一步改进视觉类分类。同样，监督分类显示出令人满意的结果，准确率始终大于 0.9。另一方面，无监督分类发现由于使用的采样方法可能低估了某些微塑料形状类别，从而成为一种有用的工具，可以通过微塑料研究中采用的传统研究方法得出无法检测到的信息。总之，拟议的应用程序提供了一种可靠的基于图片中捕获的颗粒计数、尺寸分布和形态的微塑料量化自动化方法，在方法标准化方面具有相当大的前景。