A Low-Cost Efficient System for Monitoring Microalgae Density Using Gaussian Process

Nguyen, DK; Nguyen, L; Viet Le, D

A Low-Cost Efficient System for Monitoring Microalgae Density Using Gaussian Process

Nguyen, DK Nguyen, L

Viet Le, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2021, 70, pp. 1-8
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, DK
dc.contributor.author	Nguyen, L https://orcid.org/0000-0001-5360-886X
dc.contributor.author	Viet Le, D
dc.date.accessioned	2022-02-23T06:05:29Z
dc.date.available	2022-02-23T06:05:29Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2021, 70, pp. 1-8
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/154802
dc.description.abstract	This article presents a low-cost system for efficiently monitoring the density of microalgae in a closed cultivation system, such as a photobioreactor. In fact, microalgal density can be accurately determined by manually counting methods, such as the direct microscopic count technique. However, the manual approaches are cumbersome, time-consuming, and impractical to be implemented in a closed cultivation system. Therefore, in the proposed monitoring system, microalgae are first proposed to be pumped from a culturing tank into a sample container placed inside a dark box. A low-cost camera is utilized to capture images of microalgae through the transparent sample container under artificial light. It is then proposed to represent microalgal density through two average pixel values of red and green color channels of the corresponding image. Moreover, the Gaussian process (GP) is exploited to statistically learn a data-driven model of microalgae density given the measured images. The learned model can then be used to effectively predict the density of microalgae where only their corresponding image data are required. The proposed approach was evaluated in a real-world closed bioreactor system of culturing Chlorella vulgaris microalgae, where the model was trained by 100 images selected randomly from 125 ones. In 10 000 random runs, the accuracy of the estimated density results is about 8.6% (±1.8%).
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2021.3119142
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0299 Other Physical Sciences, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	A Low-Cost Efficient System for Monitoring Microalgae Density Using Gaussian Process
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0299 Other Physical Sciences
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-02-23T06:05:27Z
pubs.publication-status	Published
pubs.volume	70

Abstract:

This article presents a low-cost system for efficiently monitoring the density of microalgae in a closed cultivation system, such as a photobioreactor. In fact, microalgal density can be accurately determined by manually counting methods, such as the direct microscopic count technique. However, the manual approaches are cumbersome, time-consuming, and impractical to be implemented in a closed cultivation system. Therefore, in the proposed monitoring system, microalgae are first proposed to be pumped from a culturing tank into a sample container placed inside a dark box. A low-cost camera is utilized to capture images of microalgae through the transparent sample container under artificial light. It is then proposed to represent microalgal density through two average pixel values of red and green color channels of the corresponding image. Moreover, the Gaussian process (GP) is exploited to statistically learn a data-driven model of microalgae density given the measured images. The learned model can then be used to effectively predict the density of microalgae where only their corresponding image data are required. The proposed approach was evaluated in a real-world closed bioreactor system of culturing Chlorella vulgaris microalgae, where the model was trained by 100 images selected randomly from 125 ones. In 10 000 random runs, the accuracy of the estimated density results is about 8.6% (±1.8%).

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http://hdl.handle.net/10453/154802