A Conceptual Framework for AI-Assisted Monitoring in Microalgae Cultivation: A Case Study on Synechococcus elongatus PCC 3055

Microalgae are recognized as environmentally sustainable and biologically renewable resources for the production of high-value products such as biomass, biofuels, dietary supplements, and pharmaceuticals. However, their large-scale cultivation is highly sensitive to environmental variables including light intensity, temperature, pH, and nutrient balance, often limiting process efficiency and continuity. This study proposes the integration of artificial intelligence (AI)-assisted monitoring and control systems into microalgae cultivation to address these challenges. In the proposed framework, environmental data (e.g., light intensity, temperature, pH, and optical density) are continuously collected via sensors, stored on cloud-based platforms, and analyzed using AI algorithms. Based on real-time data analytics, the system is capable of autonomously adjusting key cultivation parameters such as photoperiod, CO₂ injection rate, and nutrient concentrations. Furthermore, AI-enabled early warning mechanisms can detect abnormal growth trends or contamination risks before they impact overall productivity. The innovative aspect of this model lies in its integration of atmospheric circulation patterns—specifically the Hadley, Ferrel, and Polar cells—into cultivation strategy. By aligning microalgal production systems with region-specific meteorological forecasts, AI models can support adaptive planning across diverse climatic zones. For instance, tropical regions influenced by the Hadley cell favor species like Spirulina and Chlorella, while mid-latitudes under Ferrel cell dynamics require more adaptable species such as Ulva or Synechococcus elongatus. To assess the feasibility of the proposed concept, a pilot-scale cultivation of Synechococcus elongatus PCC 3055 was conducted under controlled conditions (4800–5200 lux, 28 °C) over eight days with six replicates. On day 8, the culture reached optical density values of OD₆₈₀ = 1.616 and OD₇₃₀ = 1.108, indicating effective growth under the selected parameters. These findings highlight the critical role of continuous environmental monitoring in supporting AI-based decision systems. In conclusion, this study presents a climate-aware, AI-driven, and scalable model for microalgae cultivation. Future developments will focus on training the system with real-time atmospheric datasets and implementing autonomous field applications for industrial-scale deployment.