## 8 Classification of Monitoring Techniques and Principles for Their Application
Table 8 outlines the integrated monitoring model for microalgae management (IMMM), a five-layer conceptual structure that reflects the increasing sophistication and automation of monitoring systems. Each layer corresponds to a specific functional and technological advancement [261]. At the base, basic sensing refers to the real-time measurement of essential environmental conditions, which is the minimum requirement for any controlled microalgae culture. Bio-functional monitoring builds on this by incorporating non-invasive assessments of physiological status--e.g., growth rates and intracellular compounds--using advanced techniques such as fluorometry and NIR. As cultivation systems evolve, predictive diagnostics introduce machine learning models that analyse time series data to predict critical events or shifts in biomass productivity. These diagnoses form the basis for the autonomous control layer, in which AI algorithms take over important operational decisions such as nutrient dosing or CO\({}_{2}\) injection. At the top of the model is scalability and adaptive optimisation, which requires open, flexible architectures capable of integrating new sensors, databases, and control strategies as they become available [261]. This theoretical framework provides a practical vision for incrementally improving the intelligence and robustness of algae cultivation systems (Table 8). The key research and technology priorities that have emerged from the review conducted and the model proposed are listed in Table 9. They aim to accelerate the implementation of advanced monitoring technologies under industrial conditions. One of the main challenges is the standardisation and validation of monitoring methods to enable comparability of results between different laboratories and systems. Equally important is the integration of sensors and ensuring their interoperability, which enables the creation of modular and scalable platforms. The use of artificial intelligence and adaptive prediction can enable precise management of the cultivation process, customised to a specific strain and environmental conditions [242]. Cost and scalability analyses are also necessary to assess the profitability of investments in intelligent monitoring systems. Finally, the development of open databases and IoT platforms promotes collaboration, transparency, and knowledge sharing between research teams and industry. The identified directions define a strategic action plan for the development of modern microalgae cultivation technologies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Framework Layer** & **Functional Description** & \begin{tabular}{c} **Key Technologies/** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Operational Objective** \\ \end{tabular} \\ \hline Real-Time Monitoring & \begin{tabular}{c} Continuous tracking of \\ environmental and \\ cultural parameters \\ \end{tabular} & \begin{tabular}{c} pH, temperature, CO\({}_{2}\)/O\({}_{2}\) \\ sensors, turbidity, RGB, \\ NIR sensors \\ \end{tabular} & \begin{tabular}{c} Early detection of process \\ deviations; \\ stability maintenance \\ \end{tabular} \\ \hline AI-Based Optimisation & \begin{tabular}{c} Dynamic adjustment of \\ cultivation conditions \\ using real-time and \\ historical data \\ \end{tabular} & \begin{tabular}{c} Machine learning \\ algorithms, predictive \\ models, metaheuristics \\ \end{tabular} & \begin{tabular}{c} Maximisation of growth \\ rate and \\ metabolite synthesis \\ \end{tabular} \\ \hline Data Integration & \begin{tabular}{c} Cross-sensor data \\ integration and \\ visualisation for \\ process insight \\ \end{tabular} & \
\begin{tabular}{c} IoT systems, cloud \\ analytics, digital \\ dashboards, process \\ control software \\ \end{tabular} &
\begin{tabular}{c} Centralised control and \\ long-term decision support \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Proposed theoretical framework: integrated monitoring model for microalgae cultivation (IMMM).