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Efficient plastic detection in coastal areas with selected spectral bands.

Ámbar Pérez-García1, Tim H M van Emmerik2, Aser Mata3

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This study identifies key spectral bands in the visible and near-infrared range for detecting marine plastic pollution using machine learning. Findings support developing affordable sensors for effective plastic detection and mitigation.

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Area of Science:

  • Environmental Science
  • Remote Sensing Technology
  • Machine Learning Applications

Background:

  • Marine plastic pollution presents significant ecological and economic challenges.
  • Spectral imaging and optical remote sensing are valuable for detecting aquatic macroplastics.
  • Shortwave infrared sensors are effective but costly, hindering widespread application.

Purpose of the Study:

  • To identify optimal spectral bands within the visible and near-infrared (VNIR) range for marine plastic detection.
  • To assess the transferability of machine learning models across diverse plastic datasets.
  • To inform the development of cost-effective sensors for marine plastic monitoring.

Main Methods:

  • Utilized Sequential Feature Selection (SFS) and Random Forest (RF) models for band selection.
  • Evaluated models across four datasets: laboratory virgin plastics to field-weathered plastics.
  • Assessed model performance and band transferability between datasets.

Main Results:

  • Achieved 97% accuracy in plastic detection with homogeneous backgrounds.
  • Demonstrated successful band transferability between datasets, ranging from 87% to 91%.
  • Indicated that model transfer requires further dataset-specific training for optimal accuracy.

Conclusions:

  • VNIR spectral range holds potential for cost-effective marine plastic detection.
  • Machine learning models show promise for broad sensor application with further refinement.
  • This research supports the development of affordable sensors to combat global marine plastic pollution.