{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T20:17:55Z","timestamp":1781295475219,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Council of Higher Education of Turkey","award":["YUDAB Scholarship"],"award-info":[{"award-number":["YUDAB Scholarship"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods\u2014such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches\u2014while 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\u2013driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation.<\/jats:p>","DOI":"10.3390\/bdcc9060153","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T04:23:02Z","timestamp":1749442982000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Real-Time Algal Monitoring Using Novel Machine Learning Approaches"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3994-8099","authenticated-orcid":false,"given":"Seyit","family":"Uguz","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA"},{"name":"Biosystems Engineering, Faculty of Engineering-Architecture, Yozgat Bozok University, Yozgat 66100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yavuz Selim","family":"Sahin","sequence":"additional","affiliation":[{"name":"Department of Plant Protection, Faculty of Agriculture, Bursa Uludag University, Gorukle, Bursa 16240, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-4597","authenticated-orcid":false,"given":"Xufei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yin, Z., Zhu, L., Li, S., Hu, T., Chu, R., Mo, F., Hu, D., Liu, C., and Li, B. 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