{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T05:33:11Z","timestamp":1781933591461,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring 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<\/jats:p>","DOI":"10.3390\/s23052543","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2543","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5360-886X","authenticated-orcid":false,"given":"Linh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7237-6600","authenticated-orcid":false,"given":"Dung K.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Agriculture and Life Science, Chonnam National University, Gwangju 61186, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6814-4885","authenticated-orcid":false,"given":"Thang","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Engineering, Texas A&M University\u2013Corpus Christi, Corpus Christi, TX 78412, USA"},{"name":"Department of Automation and Control Engineering, Thuyloi University, Hanoi 116705, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2888-9732","authenticated-orcid":false,"given":"Binh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Engineering, Texas A&M University\u2013Corpus Christi, Corpus Christi, TX 78412, USA"},{"name":"Department of Automation and Control Engineering, Thuyloi University, Hanoi 116705, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4841-3800","authenticated-orcid":false,"given":"Truong X.","family":"Nghiem","sequence":"additional","affiliation":[{"name":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105641","DOI":"10.1016\/j.compag.2020.105641","article-title":"Low-cost and versatile sensor based on multi-wavelengths for real-time estimation of microalgal biomass concentration in open and closed cultivation systems","volume":"176","author":"Soares","year":"2020","journal-title":"Comput. 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