Analysis of microalgal density estimation by using lasso and image texture features
- Authors: Nguyen, Linh , Nguyen, Dung , Nguyen, Thang , Nguyen, Binh , Nghiem, Truong
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 5 (2023), p.
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- Description: 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.
A low-cost system for monitoring pH, dissolved oxygen and algal density in continuous culture of microalgae
- Authors: Nguyen, Dung , Nguyen, Huy , Dang, Huyen , Nguyen, Viet , Nguyen, Linh
- Date: 2022
- Type: Text , Journal article
- Relation: HardwareX Vol. 12, no. (2022), p.
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- Description: In a continuous and closed system of culturing microalgae, constantly monitoring and controlling pH, dissolved oxygen (DO) and microalgal density in the cultivation environment are paramount, which ultimately influence on the growth rate and quality of the microalgae products. Apart from the pH and DO parameters, the density of microalgae can be used to contemplate what light condition in the culture chamber is or when nutrients should be supplemented, which both also decide productivity of the cultivation. Moreover, the microalgal density is considered as an indicator indicating when the microalgae can be harvested. Therefore, this work proposes a low-cost monitoring equipment that can be employed to observe pH, DO and microalgal density over time in a culture environment. The measurements obtained by the proposed monitoring device can be utilized for not only real-time observations but also controlling other sub-systems in a continuous culture model including stirring, ventilating, nutrient supplying and harvesting, which leads to more efficiency in the microalgal production. More importantly, it is proposed to utilize the off-the-shelf materials to fabricate the equipment with a total cost of about 513 EUR, which makes it practical as well as widespread. The proposed monitoring apparatus was validated in a real-world closed system of cultivating a microalgae strain of Chlorella vulgaris. The obtained results indicate that the measurement accuracies are 0.3%, 3.8% and 8.6% for pH, DO and microalgae density quantities, respectively. © 2022 The Author(s)
Least square and Gaussian process for image based microalgal density estimation
- Authors: Nguyen, Linh , Nguyen, Dung , Nghiem, Truong , Nguyen, Thang
- Date: 2022
- Type: Text , Journal article
- Relation: Computers and Electronics in Agriculture Vol. 193, no. (2022), p.
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- Description: Efficiently monitoring microalgal density in real time is critical in closed systems of cultivating algae. In the monitoring methods proposed in the literature, image based techniques present practically potential since they are nondestructive and more biosecured. However, in the existing image analysis methods, parameters of the color-to-grayscale conversion formulae are predefined and only applicable to monitor some specific microalgae strains. Therefore, in this paper we propose a generic approach based on least square to optimize those parameters, which are data-driven and can be used to monitor any type of microalgae. More importantly, apart from the widely used linear regression paradigm, we propose a nonlinear regression model based on Gaussian process to better learn relationship between data representation of measured images and densities of microalgae. The nonlinear regression model is then utilized to efficiently estimate density of algal species. The proposed approach was evaluated in the real-world dataset of Chlorella vulgaris microalgae, where the obtained results as compared with those obtained by some existing techniques demonstrate its effectiveness. © 2022 Elsevier B.V.
A low-cost efficient system for monitoring microalgae density using gaussian process
- Authors: Nguyen, Dung , Nguyen, Linh , Viet Le, Dung
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Instrumentation and Measurement Vol. 70, no. (2021), p.
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- Description: 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%). © 1963-2012 IEEE.