Journal of Computer Technology and Software

Vol. 1 No. 1 (2024)
Articles

Advancing Soil Microplastics Detection: Insights from Hyperspectral Imaging Technology

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  • Cheng Chen,
  • Jiawei Zhang ,
  • Jie Guo

Published 2024-02-28

Keywords

  • Microplastics; Marine pollution; Hyperspectral; Support vector machine.

How to Cite

Chen, C., Zhang , J., & Guo, J. (2024). Advancing Soil Microplastics Detection: Insights from Hyperspectral Imaging Technology. Journal of Computer Technology and Software, 1(1), 17–21. Retrieved from https://ashpress.org/index.php/jcts/article/view/21
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Addressing the rising concern of microplastic pollution in soil, this study proposes employing hyperspectral imaging technology for efficient detection. Utilizing supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MD), and Maximum Likelihood (ML), microplastic pollutants in soil are directly identified and classified. Experiments conducted within a wavelength range of 400-1000 nm reveal SVM as the most suitable algorithm, achieving an average identification accuracy of 84% for white polyethylene (PE) microplastics within the 1-5 mm particle size range.

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