###### Abstract
This can help reduce crop production costs and increase productivity [14]. AI methods address these big data-related challenges [15]. Machine learning (ML), a subset of AI, is widely used to identify hidden patterns in the data. ML can detect data whose data patterns are unknown and direct researchers to achieve the expected goals. The application of the ML Algorithm in the smart farm and greenhouse has attracted the interest of researchers. Traditional methods are not capable of analyzing large and unstructured data. In addition, it cannot identify and predict the most influential factors, especially regarding supply chain performance. Therefore, researchers replaced traditional analytical methods with machine learning techniques. ML has advantages like big data analysis and solving nonlinear problems like data-driven models [16; 17]. A data-driven model is usually a time series with a daily cyclical pattern [18]. It means that, for most applications, data profiles such as electricity charging, panel voltage, temperature, and wind turbine generation repeat their pattern every 24 h. Although, the drawback of time series analysis is that it can only be applied when a unique period in the time series exists with an adaptive law [19]. Using specific ML algorithms can minimize the disadvantages of time series patterns, such as Autoregressive (AR), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA) model, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Traditional machine learning methods include Support Vector Machine (SVM), random forest, Back Propagation (BP) neural network, and Linear Regression (LR), whereas deep learning methods include Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) [20]. Climate forecasting in greenhouses to predict microclimate conditions has become highly relevant due to the numerous sensors and systems that allow for accurate measurements and evaluations of the microclimate within seconds [21]. While different techniques have been developed to model temperature behavior inside greenhouses, they often only use variable monitoring instead of forecasts. As a result, automatic control implementation to maintain optimal microclimate conditions during the different crop stages is limited to corrective and non-preventive mitigation actions, which may only partially satisfy the needs of greenhouse growers. However, AI-based algorithms have been developed to act preventively by adjusting heating/cooling systems, ventilation, and carbonic fertilization supply through actuators installed to ensure optimal growth, maintenance, and plague control of crops in greenhouses [22]. Adding a preventive model to an automatic control integrated into greenhouses systems would make maintaining the optimal temperature for each crop more feasible. Going to extreme temperatures could represent various crop problems like changing the crops' morphology and physiological processes, such as floral formation, leaf burn, poor fruit quality, excess transpiration, and shortening crop life. Therefore, having the best possible control of the greenhouse microclimate is crucial to prevent developing pathogens and damaging crops growing [23]. This paper proposes an approach involving supervised learning algorithms on weather data from a controlled greenhouse environment using LR and SVM models, as the literature shows their vast use in time series applications. We aim to obtain a model capable of understanding the data structure to make a precise temperature forecast inside the greenhouse. These models have low computational power compared with more complex models like deep neural networks, so they are suitable for this application. Also, they are appropriate for applications where fast response is required. The contributions of this paper can be summarized as follows: Use supervised learning algorithms to study weather data from a controlled greenhouse environment.