## 8 Classification of Monitoring Techniques and Principles for Their Application  
To support the development of smart and scalable monitoring strategies for the cultivation of microalgae, this review presents an original conceptual framework that integrates technological, functional, and practical perspectives. The diversity of monitoring methods currently used in microalgae biotechnology requires a structured classification that reflects not only their analytical focus but also the level of technological readiness, degree of invasiveness, and economic feasibility. Techniques range from low-cost, widely available sensors for measuring basic environmental parameters to sophisticated, AI-powered diagnostic systems capable of performing biofunctional analyses in real time and autonomous control. These systems vary considerably in their precision, complexity, and suitability for use at scales ranging from laboratory-scale studies to full industrial deployment. A comprehensive classification of monitoring technologies used in microalgae cultivation, structured along four key dimensions--the type of parameters monitored, the technological readiness level (TRL), the cost and accessibility, and the degree of system integration--is presented in Table 7. Physico-chemical sensors, such as pH, temperature, and gas sensors, continue to form the backbone of cultivation monitoring due to their sophistication and simplicity. However, biological and optical instruments such as flow cytometry, spectroscopy (NIR, FTIR), and RGB image analysis offer more comprehensive data that provide insight into biomass dynamics and metabolite accumulation [260]. The table also highlights the discrepancy in technological readiness, where only basic sensors are fully operational (TRL 8-9), while AI-integrated systems and advanced analytical platforms are still at the experimental or pilot stage (TRL 3-6). In addition, economic feasibility is a major barrier to implementation: low-cost tools are suitable for routine use, while high-end systems are limited to well-funded laboratories or demonstration facilities. This classification facilitates strategic decisions about which technologies are suitable for specific scales, objectives, and resource constraints. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **Category** & **Examples of Tools/Technologies** \\ \hline \multirow{7}{*}{Type of monitored parameters} & \multirow{2}{*}{Physicochemical} & pH, temperature, CO\({}_{2}\), O\({}_{2}\) sensors; light intensity; turbidity sensors \\ \cline{2-3}  & & \multirow{2}{*}{Biological} & Flow cytometry, fluorometry, in situ microscopy, chlorophyll analysis \\ \cline{2-3}  & & \multirow{2}{*}{Optical} & RGB imaging, UV-VIS, FTIR/NIR spectroscopy, image-based diagnostics \\ \cline{2-3}  & & Multiparameter integrated systems & IoT platforms, sensor arrays, software-based sensors \\ \hline \multirow{7}{*}{Technology readiness level} & \multirow{2}{*}{TRL 1–3: early-stage research} & Advanced spectroscopy, online flow cytometry, AI-based diagnostics \\ \cline{2-3}  & & \multirow{2}{*}{TRL 4–6: prototyping and pilot testing} & Hybrid AI-IoT platforms, semi-automated monitoring systems \\ \cline{2-3}  & & \multirow{2}{*}{TRL 7–9: mature and deployable technologies} & Basic sensors for pH, temperature, light, turbidity \\ \cline{2-3}  & & \multirow{2}{*}{Low-cost} & RGB cameras, basic environmental sensors (pH, temperature, light) \\ \cline{2-3}  & & \multirow{2}{*}{Mid-cost} & Fluorometers, UV-VIS, PAR sensors \\ \cline{2-3}  & & \multirow{2}{*}{High-cost} & Flow cytometers, FTIR/NIR spectroscopy, AI-integrated platforms \\ \cline{2-3}  & & \multirow{2}{*}{Mid-cost} & \multirow{2}{*}{Fluoreometers, UV-VIS, PAR sensors} \\ \cline{2-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification framework for microalgae cultivation monitoring technologies.