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In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses
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Cost-effective wireless system for image-based microalgae biomass estimation with adaptive background correction.

Hua Xiao1, Qiaoyang Zhang2, Haiyun Chen1

  • 1School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Bioresource Technology
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Accurate microalgae biomass estimation is now possible with a new adaptive background-corrected framework. This low-cost system improves RGB-based biomass estimation consistency under varied conditions, offering a cost-effective solution for cultivation monitoring.

Keywords:
Adaptive background correctionBackground interference eliminationMicroalgae biomass assessmentPolynomial regressionRemote-controlled sensing

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Area of Science:

  • Biotechnology
  • Agricultural Engineering
  • Optical Sensing

Background:

  • Accurate microalgae biomass estimation is crucial for optimizing cultivation.
  • Conventional RGB imaging methods struggle with background variations.
  • Existing techniques lack consistency under diverse imaging conditions.

Purpose of the Study:

  • To develop an adaptive background-corrected framework for robust image-based microalgae biomass estimation.
  • To enhance the consistency of RGB-based biomass estimation despite background chromaticity changes.
  • To create a cost-effective and portable solution for microalgae cultivation monitoring.

Main Methods:

  • Polynomial RGB regression analysis was employed for adaptive background correction.
  • A low-cost wireless sensing system (<$200) with adjustable LED illumination and portable colorimetric sensing was utilized.
  • Three background-correction algorithms (ratio, offset, linear) were integrated and tested.

Main Results:

  • The proposed framework achieved high cross-background average accuracies of 0.94 for Nannochloropsis sp. and 0.90 for Isochrysis galbana.
  • Adaptive background correction significantly reduced interference from background chromaticity.
  • The system demonstrated improved consistency in biomass estimation across different imaging conditions.

Conclusions:

  • Adaptive background correction is an effective strategy to overcome limitations in RGB-based microalgae biomass estimation.
  • The developed low-cost sensing system offers a practical and affordable tool for portable microalgae cultivation monitoring.
  • This approach enhances the reliability of image-based monitoring for microalgae production.