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Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features.

Linh Nguyen1, Dung K Nguyen2, Thang Nguyen3,4

  • 1Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

Accurate microalgae density estimation is vital for cultivation. This study introduces advanced texture features and a LASSO model for precise monitoring of Chlorella vulgaris, outperforming existing methods.

Keywords:
LASSOalgal monitoringimage processingimage texture featuresmicroalgaemicroalgal density

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Area of Science:

  • Algaculture
  • Biotechnology
  • Image analysis

Background:

  • Accurate microalgae density monitoring is crucial for optimizing cultivation conditions and nutrient control in closed systems.
  • Image-based methods offer non-invasive, non-destructive, and biosecure alternatives for microalgae estimation.
  • Traditional image-based methods often rely on simple pixel averaging, potentially missing rich textural information.

Purpose of the Study:

  • To develop a more accurate and robust method for estimating microalgae density in closed cultivation systems.
  • To leverage advanced image texture features beyond simple pixel averaging for improved estimation.
  • To apply a data-driven model, specifically L1 regularization (LASSO), for feature selection and density prediction.

Main Methods:

  • Extraction of advanced texture features from microalgae images, including confidence intervals of pixel value means, spatial frequency powers, and pixel distribution entropies.
  • Utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) model for data-driven analysis and feature selection, prioritizing informative texture features.
  • Validation of the proposed method using real-world experiments with the *Chlorella vulgaris* microalgae strain.

Main Results:

  • The proposed texture feature-based LASSO model achieved superior performance in estimating *Chlorella vulgaris* density.
  • The average estimation error using the proposed approach was 1.54.
  • This significantly outperformed traditional methods, with average errors of 2.16 for Gaussian process and 3.68 for gray-scale-based approaches.

Conclusions:

  • Advanced image texture features combined with a LASSO model provide a highly accurate method for microalgae density estimation.
  • The proposed approach offers a significant improvement over existing techniques, enabling better control and optimization of algal cultivation.
  • This method holds promise for enhancing efficiency and productivity in commercial microalgae farming.