Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics |
| Abstract | 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. |
| Source | Water (Basel) 13 (15) |
| Keywords | microplastics; computer vision; machine learning; automatic; quantification; classification |
| Journal | Water (Basel) |
| Editor | Molecular Diversity Preservation International, Basel, |
| Year | 2021 |
| Type | Articolo in rivista |
| DOI | 10.3390/w13152104 |
| Authors | Carmine Massarelli, Claudia Campanale, Vito Felice Uricchio |
| Text | 455670 2021 10.3390/w13152104 microplastics; computer vision; machine learning; automatic; quantification; classification A Handy Open Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics Carmine Massarelli, Claudia Campanale, Vito Felice Uricchio Water Research Institute Italian National Research Council IRSA CNR , 70132 Bari, Italy 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. 13 Published version https //www.mdpi.com/2073 4441/13/15/2104 Articolo in rivista Molecular Diversity Preservation International 2073 4441 Water Basel Water Basel Water Basel Water. Basel claudiacampanale CAMPANALE CLAUDIA vitofelice.uricchio URICCHIO VITO FELICE carmine.massarelli MASSARELLI CARMINE |