Analysis of microalgal density estimation by using LASSO and image texture features

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dc.contributor.authorNguyen, Linh
dc.contributor.authorNguyen, Dung Kim
dc.contributor.authorNguyen, Thang
dc.contributor.authorNguyen, Thanh Binh
dc.contributor.authorNghiem, Truong
dc.creator.orcidhttps://orcid.org/0000-0001-6814-4885en_US
dc.creator.orcidhttps://orcid.org/0000-0002-2888-9732en_US
dc.creator.orcidhttps://orcid.org/0000-0001-6814-4885
dc.creator.orcidhttps://orcid.org/0000-0002-2888-9732
dc.date.accessioned2023-03-01T18:53:49Z
dc.date.available2023-03-01T18:53:49Z
dc.date.issued2023-02-24
dc.description.abstractMonitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively.en_US
dc.description.abstractMonitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively.
dc.identifier.citationNguyen, L., Nguyen, D. K., Nguyen, T., Nguyen, B., & Nghiem, T. X. (2023). Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features. Sensors, 23(5), 2543. https://doi.org/10.3390/s23052543en_US
dc.identifier.citationNguyen, L., Nguyen, D. K., Nguyen, T., Nguyen, B., & Nghiem, T. X. (2023). Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features. Sensors, 23(5), 2543. https://doi.org/10.3390/s23052543
dc.identifier.doihttps://doi.org/10.3390/s23052543
dc.identifier.urihttps://hdl.handle.net/1969.6/95539
dc.language.isoen_USen_US
dc.language.isoen_US
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectmicroalgaeen_US
dc.subjectmicroalgal densityen_US
dc.subjectLASSOen_US
dc.subjectimage texture featuresen_US
dc.subjectimage processingen_US
dc.subjectalgal monitoringen_US
dc.subjectmicroalgae
dc.subjectmicroalgal density
dc.subjectLASSO
dc.subjectimage texture features
dc.subjectimage processing
dc.subjectalgal monitoring
dc.titleAnalysis of microalgal density estimation by using LASSO and image texture featuresen_US
dc.titleAnalysis of microalgal density estimation by using LASSO and image texture features
dc.typeArticleen_US
dc.typeArticle

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