Real-Time Algal Monitoring Using Novel Machine Learning Approaches
Open accessAbstract
Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R2 = 0.77), while RF demonstrated superior performance for dry biomass estimation (R2 = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation.
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Biological and optical instruments such as flow cytometry, spectroscopy (NIR, FTIR), and RGB image analysis offer more comprehensive data that provide insight into biomass dynamics and metabolite accumulation.
“However, biological and optical instruments such as flow cytometry, spectroscopy (NIR, FTIR), and RGB image analysis offer more comprehensive data that provide insight into biomass dynamics and metabolite accumulation [260].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [260]
The Introduction identifies flow cytometry and spectroscopy as algal monitoring methods, and the Results and Discussion link RGB image analysis to biomass concentration. The cited paper does not specify NIR or FTIR, establish that these methods provide more comprehensive data, or show that they reveal metabolite accumulation. Page numbers were not available in our copy. (LLM, conf=0.86)
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- Authors
- Uguz, S., Sahin, Y. S., Kumar, P., Yang, X., & Anderson, G.
- Affiliations
- Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA
- Department of Plant Protection, Faculty of Agriculture, Bursa Uludag University, Gorukle, Bursa 16240, Turkey
- South Dakota State University (US)
- Corresponding author
- Anderson, Gary
- Venue
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Published
- 2025-06-09
- Volume
- 9
- Issue
- 6
- Pages
- 153
- ISSN
- 2504-2289 (ISSN-L)
- OpenAlex
- W4411157788
- Cited by
- 20
- References
- 43
- Citations by year
- 2026: 13 · 2025: 7
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data and Cognitive Computing (publishedVersion, cc-by)
- Bursa Uludag University - AVESIS (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- The Council of Higher Education of Turkey (YUDAB Scholarship)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- photobioreactors, Scenedesmus dimorphus, cell count estimation, RGB color histogram, vision-based monitoring, real-time monitoring, decision tree, random forest, gradient boosting, k-nearest neighbors, low-cost monitoring
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