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Traditional microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic methods are accurate but costly, time-consuming, labor-intensive, and vulnerable to contamination or production interference. 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Real-Time Algal Monitoring Using Novel Machine Learning Approaches. Big Data Cogn. Comput. 2025, 9, 153 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)bdcc9060153  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA; [seyit@uludag.edu.tr](seyit@uludag.edu.tr) (S.U.); [pradeep.kumar4920@sdstate.edu](pradeep.kumar4920@sdstate.edu) (P.K.); [xufei.yang@sdstate.edu](xufei.yang@sdstate.edu) (X.Y.)  \n2 Biosystems Engineering, Faculty of Engineering-Architecture, Yozgat Bozok University, Yozgat 66100, Turkey  \n3 Department of Plant Protection, Faculty of Agriculture, Bursa Uludag University, Gorukle, Bursa 16240, Turkey; [yavuzselim.xyz@gmail.com](yavuzselim.xyz@gmail.com)  \n* Correspondence: [gary.anderson@sdstate.edu](gary.anderson@sdstate.edu)  \nAbstract: 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.  \nKeywords: biomass; cell concentration; photobioreactor; image analysis; Scenedesmus dimorphus  \n1. Introduction  \nMicroalgae can efficiently utilize carbon dioxide (CO 2) as a carbon source and light as an energy source to produce phospholipids, proteins, nucleic acids, and lipids. These lipids can be further converted into biodiesel through transesterification processes. Asa renewable energy source, microalgae offer significant economic and environmental advantages due to their high photosynthetic efficiency, rapid growth rates, and substantial biomass productivity. Additionally, microalgae can effectively mitigate gas pollutants such as ammonia (NH 3), CO 2, nitrogen oxides (NOx ), and sulfur oxides (SOx ), converting them into valuable products such as animal feed [1–3] .  \nAccurate estimation of algal cell concentration (cells mL−1) and dry algal biomass (g L −1) is essential for evaluating algal growth and productivity. However, traditional monitoring methods, such as gravimetric biomass analysi","cbCaisY1vazIw3k1","https://ap.wps.com/l/cbCaisY1vazIw3k1","pdf",2821577,"English","# Introduction\n## Background and need for automated monitoring\n## Digital imaging and color-based models\n## Machine learning for image prediction","[{\"question\":\"Why is real-time monitoring of microalgae important in photobioreactors?\",\"answer\":\"Accurate estimation of cell concentration and dry biomass is essential for evaluating growth and productivity, while real-time process control supports scalability in continuous or large-scale production.\"},{\"question\":\"What limitations affect conventional algal monitoring methods?\",\"answer\":\"Gravimetric biomass and microscopic cell counting are labor-intensive, time-consuming, expensive, and prone to contamination due to manual sampling from photobioreactors.\"},{\"question\":\"How does the proposed study estimate algae parameters?\",\"answer\":\"It uses machine learning models trained on RGB color histogram features extracted from images of Scenedesmus dimorphus cultures, with ground truth from manual cell enumeration and dry biomass measurements.\"}]","Real-Time Algal Monitoring Using Novel Machine Learning Approaches | PDF",48]