# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 5 Conclusions and Future Works  
The aim of this work was to propose a particle swarm optimization model predictive control (PSO-MPC) algorithm for greenhouse control. This algorithm was designed to optimize the controllable parameters of a greenhouse, such as heating, ventilation, and lighting levels, by using a combination of a greenhouse climatic model and a biophysical model. The goal was to maximize crop yield while minimizing energy costs. To achieve this goal, the PSO-MPC algorithm leverages a model predictive-control-based approach that exploits particle swarm optimization, which is s a heuristic optimization method that is inspired by the behavior of social swarms, such as bird flocks or fish schools, to determine the optimal controllable parameters for greenhouse control. It uses a population of particles that move through the search space to find the optimal solution. The PSO-MPC algorithm outperformed traditional control algorithms by leveraging real weather conditions and greenhouse configuration information. The results of the study show that the PSO-based optimization approach performed better than the GA for objective function optimization within the MPC framework. In future work, the proposed control approach will be evaluated in different scenarios, including different greenhouse locations, crop types, and weather conditions. This would help in validating the effectiveness of the proposed algorithm in real-world applications, and would demonstrate its potential to optimize greenhouse control in various settings. Overall, the proposed PSO-MPC algorithm is a promising approach for greenhouse control that could help in maximizing crop yield while minimizing energy costs. The use of a predictive-control-model-based approach with particle swarm optimization enables the algorithm to optimize the controllable parameters of a greenhouse in real time, ensuring that the growing conditions remain optimal for the plants. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Yield (mg/m\({}^{2}\))** & **Consumed Energy (MJ/m\({}^{2}\))** & **Energy Efficiency** \\ \hline Rule-based algorithm & 57.57 & 4.87 & 0.085 \\ Proposed algorithm & 152.17 & 7.88 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons between the MPC algorithm and traditional proportional control algorithm for 21 h. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Yield (mg/m\({}^{2}\))** & **Consumed Energy (MJ/m\({}^{2}\))** & **Energy Efficiency** \\ \hline Rule-based algorithm & 842.20 & 19.67 & 0.023 \\ Proposed algorithm & 1799.30 & 33.29 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different MPC optimization algorithms through statistical analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Yield (mg/m\({}^{2}\))** & **Consumed Energy (MJ/m\({}^{2}\))** \\ \hline GA & \(-0.0484\pm 2.2942\) & \(3.1618\pm 0.3924\) \\ PSO & \(131.9381\pm 22.3560\) & \(6.4741\pm 1.7893\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons between MPC algorithm and traditional proportional control algorithm for 41 h. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Yield (mg/m\({}^{2}\))** & **Consumed Energy (MJ/m\({}^{2}\))** & **Energy Efficiency** \\ \hline Rule-based algorithm & 57.57 & 4.87 & 0.085 \\ Proposed algorithm & 152.17 & 7.88 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons between MPC algorithm and traditional proportional control algorithm for 41 h.  
**Author Contributions:** Writing--original draft preparation, L.G. ; methodology development and validation, L.G. ; writing--review and editing, M.Y. and S.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported as part of SMARTGREEN, an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union. **Data Availability Statement:** Data are available on request.