## 6 Conclusions  
The smart farm is an integral part of the new agricultural revolution towards environmentally friendly practices, focusing on science and technology. Greenhouses are used to optimize crop growth by monitoring variables such as temperature, humidity, ventilation, solar radiation, and wind speed. However, modeling the internal environment of a greenhouse is challenging due to its dynamic nature and dependence on external conditions. Various methods for predicting temperature changes within greenhouses focus on monitoring current conditions rather than forecasting future ones. As a result, greenhouse managers are limited to corrective and non-preventive measures, which may only partially meet their needs. Fortunately, AI algorithms' forecasts are being used to analyze greenhouse behavior and execute preventive actions by adjusting heating, ventilation, and fertilization systems, guaranteeing the best possible growth, upkeep, and management of crops within the greenhouse. This research focused on predicting the temperature inside a greenhouse. Over almost a year, the Davis Vantage Pro 2 central meteorological system gathered data on internal factors (such as temperature, humidity, and dew point) and external factors (such as temperature, humidity, and solar radiation). Various experiments were conducted to predict the temperature using four models adapted to a specific year's season. These models included MLR using OLS, multiple regression using PLS, SVR with RBF, and polynomial kernel. LR models were used to analyze the correlation between input variables and the internal temperature of a greenhouse. However, these models can have difficulty adjusting to temperature spikes. The SVR models, which are more complex, are better at adapting to different factors, especially in extreme temperature conditions. The SVR polynomial model had the highest forecast accuracy of all the algorithms. The greenhouse control system will utilize these models to give accurate instructions to the different actuators and anticipate upcoming needs, thereby maintaining the optimal internal conditions for the crops. Conceptualization, H.A.G.-O., J.R.P.-G., F.G.-V. and L.F.L.-V.; methodology, R.C.-N., H.A.G.-O. and L.F.L.-V.; software, J.R.P.-G. and F.G.-V.; validation, J.R.P.-G., M.d.R.M.-B., R.C.-N. and G.D.-F.; formal analysis, F.G.-V.; investigation, J.R.P.-G.; resources, J.R.P.-G., F.G.-V., G.D.-F., C.L.C.-M. and M.E.M.-R.; data curation, J.R.P.-G.; writing--original draft preparation, F.G.-V., J.R.P.-G. and R.C.-N.; writing--review and editing, H.A.G.-O., L.F.L.-V., M.E.M.-R., M.d.R.M.-B., G.D.-F. and C.L.C.-M.; visualization, M.d.R.M.-B., M.E.M.-R. and C.L.C.-M.; supervision, H.A.G.-O., and R.C.-N.; project administration, H.A.G.-O. and R.C.-N.; funding acquisition, H.A.G.-O., R.C.-N. and L.F.L.-V. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Model** & **MAE** & **RMSE** \\ \hline [PERSON] et al. [32] & SVM & - & 2.77 \\ [PERSON] et al. [34] & Fuzzy Logic SVM & 0.609 & 1.07 \\ [PERSON] et al. [35] & SVM & 0.162 & 0.218 \\ [PERSON] et al. [33] & SVM BRF & - & 0.0137 \\ [PERSON] et al. & SVM Polynomial & 0.0422 & 0.0549 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SVR models comparison presented in related works. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data supporting the reported results can be found at [[https://github.com/fabiangraciauaz/GreenhouseData](https://github.com/fabiangraciauaz/GreenhouseData)]([https://github.com/fabiangraciauaz/GreenhouseData](https://github.com/fabiangraciauaz/GreenhouseData)) (accessed on 19 June 2023).