- Title
- Analysis of microalgal density estimation by using lasso and image texture features
- Creator
- Nguyen, Linh; Nguyen, Dung; Nguyen, Thang; Nguyen, Binh; Nghiem, Truong
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193593
- Identifier
- vital:18216
- Identifier
-
https://doi.org/10.3390/s23052543
- Identifier
- ISSN:1424-8220 (ISSN)
- Abstract
- 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 © 2023 by the authors.
- Publisher
- MDPI
- Relation
- Sensors Vol. 23, no. 5 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems software; Algal monitoring; Image processing; Image texture features; LASSO; Microalgae; Microalgal density
- Full Text
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