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<p>Machine learning (ML) has been used in microalgae cultivation and processing for optimizing bioprocesses, reducing costs, and enhancing productivity. ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning systems have been extensively utilized for tasks including species identification, biomass quantification, and process optimization. These tools enable the handling of complex datasets, integrating parameters such as light intensity, nutrient concentration, and growth kinetics to predict and enhance outcomes. Recent advancements, including the use of imaging techniques, hyperspectral analysis, and data-driven models, have improved microalgae productivity under variable environmental conditions. Moreover, ML-driven innovations in harvesting processes such as flocculation, filtration, and electrocoagulation have shown to enhance biomass recovery. However, integrating ML with real-time monitoring systems and managing large-scale operations under dynamic environmental conditions remains a challenge. This review highlights the current state of ML applications in microalgae research while outlining the challenges, and providing insights into future possibilities for advancing sustainable microalgae-based biotechnologies.</p>

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Machine learning approaches for smart cultivation and processing of microalgae

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Ranjna Sirohi,
  • Young Joon Sung,
  • Sabeela Beevi Ummalyma,
  • Ashiwin Vadiveloo,
  • Harish Chandra Yadav,
  • Ayon Tarafdar

摘要

Machine learning (ML) has been used in microalgae cultivation and processing for optimizing bioprocesses, reducing costs, and enhancing productivity. ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning systems have been extensively utilized for tasks including species identification, biomass quantification, and process optimization. These tools enable the handling of complex datasets, integrating parameters such as light intensity, nutrient concentration, and growth kinetics to predict and enhance outcomes. Recent advancements, including the use of imaging techniques, hyperspectral analysis, and data-driven models, have improved microalgae productivity under variable environmental conditions. Moreover, ML-driven innovations in harvesting processes such as flocculation, filtration, and electrocoagulation have shown to enhance biomass recovery. However, integrating ML with real-time monitoring systems and managing large-scale operations under dynamic environmental conditions remains a challenge. This review highlights the current state of ML applications in microalgae research while outlining the challenges, and providing insights into future possibilities for advancing sustainable microalgae-based biotechnologies.