## 7 Conclusions  
This study comprehensively evaluated advanced NMPC strategies integrating AI-driven components for climate control in smart greenhouse systems, emphasizing the impacts of disturbance prediction and warm-start initialization through extensive 40-day simulations of a lettuce greenhouse model across six distinct controller configurations. The findings revealed a clear trade-off: controllers employing only real-time feedback (NMPC-C, NMPC-CW) consistently maximized final biomass and profit by maintaining elevated CO\({}_{2}\) levels, which facilitated greater CO\({}_{2}\) assimilation; however, this approach also increased constraint violations. Conversely, strategies utilizing LSTM-based disturbance prediction (NMPC-P, NMPC-PW) or ideal foresight (NMPC-I, NMPC-IW) demonstrated improved resource efficiency and enhanced adherence to operational constraints, often by adopting more cautious CO\({}_{2}\) supplementation, albeit at the expense of slightly lower yields. Crucially, warm-start initialization proved highly effective across all controller types, significantly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & **Nominal \(P_{net}\) (Hf/m\({}^{2}\))** & \(P_{net}^{+10\%}\) (Hf/m\({}^{2}\)) & \(P_{net}^{-10\%}\) (Hf/m\({}^{2}\)) \\ \hline NMPC-C & 2.658 & 2.893 & 2.423 \\ NMPC-CW & 2.663 & 2.898 & 2.428 \\ NMPC-P & 2.591 & 2.821 & 2.361 \\ NMPC-PW & 2.592 & 2.822 & 2.362 \\ NMPC-I & 2.586 & 2.815 & 2.356 \\ NMPC-IW & 2.585 & 2.815 & 2.356 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Net profit (\(P_{net}\)) intervals for all controller strategies, accounting for \(\pm\)10% biomass measurement error. reducing average solver computation times by up to 60% without any discernible impact on control performance or output quality, thereby underscoring its practical value for real-time automation in smart greenhouse control. Furthermore, the study highlighted that while economic objective weights influence the balance between productivity and resource expenditure, the satisfaction of operational constraints is predominantly governed by the selection of appropriate penalty parameters. The selection of an optimal NMPC strategy should therefore be guided by specific operational priorities, whether that is maximizing yield and economic returns, minimizing resource consumption and environmental impact, or strictly enforcing climate setpoints; regardless of the chosen control logic, the adoption of warm-start techniques is strongly recommended to ensure computational feasibility in practical real-time AI-driven smart greenhouse applications. While this research provides valuable insights, its foundation on simulation-based evaluations using a specific lettuce crop model, a validated greenhouse climate model from Wageningen Agricultural University, and particular environmental conditions (Thai Nguyen, Vietnam) underscores the necessity for further investigation. Key limitations include the simplification of real-world atmospheric CO\({}_{2}\) dynamics and microclimate variations by the synthetic CO\({}_{2}\) model and gridded NASA POWER weather data, respectively, as well as the use of a pre-trained, static ML-based weather predictor. Additionally, the NMPC formulation did not incorporate online model parameter adaptation to address potential model-plant mismatch over extended cultivation periods, focused on a single set of tuning parameters (economic weights and constraint penalties), excluded the energy cost associated with natural ventilation as it is considered negligible in the assumed model, and excluded considerations of external economic factors like market price volatility or detailed physiological aspects such as air circulation's impact on tipburn. Building upon these limitations, future research should prioritize on-site experimental validation in diverse greenhouse settings and across different crop types.