## 2 Related Work  
This section explores the current agricultural systems and discusses ways to improve them by leveraging data from different sources. The text presents the latest technological advancements in agriculture, focusing on machine-learning applications and the prediction of climatological variables within greenhouses. This information is valuable for individuals seeking to enhance agricultural practices and increase productivity. Authors in [25] implemented the LSTM-RNN to work with time series to forecast the temperature and relative humidity inside a greenhouse, considering microclimatic parameters as input components. Solar radiation, external temperature, external humidity, and wind direction were considered impact variables on the forecast. Based on this, the proposal focuses on observing the behavior of the LSTM-RNN through the variation in the number of hidden layers and analyzing the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R\({}^{2}\)) to obtain the level of efficiency of the network in the forecasting of temperature and relative humidity. In [26], the authors presented an RNN-Back-Propagation (BP) model to predict the temperature and humidity of a solar greenhouse in northern China. Climate data, such as air and substrate temperature, humidity, lighting, and carbon dioxide (CO\({}_{2}\)) concentration recorded over eight days, were used to build and validate climate prediction models. The results show that the model provides reasonably good predictions, with RSMEs of 0.751 for temperature and 0.781 for humidity, and both R\({}^{2}\) values being above 0.9, outperforming the models in this study. The study in [27] introduces a solar greenhouse evapotranspiration (ET) estimation model called PSO (Particle Swarm Optimization)-XGBoost. This model uses meteorological and soil moisture data from a greenhouse in China to grow two tomato crops. The PSO-XGBoost model accurately estimates evapotranspiration under different data configurations. The study found that the model's accuracy decreases as input variables are reduced. Moreover, the variables solar radiation and vapor pressure difference were identified as the most critical variables. The work presented in [28] aims to manage and predict the air temperature in greenhouses using a wireless sensor network for data collection. The prediction model is based on RNN techniques. The prediction algorithm utilizes weather data to predict the missing values in case of missing sensor data. The experimental results show that the RNN-based prediction algorithm can efficiently forecast the air temperature in the greenhouse with an RMSE of 1.50 \({}^{\circ}\)C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and an R\({}^{2}\) of 0.965. The study in [29] discusses using an RNN model as a deep learning architecture that can retain the recent memories of input patterns. This is achieved through the feedback mechanism of its hidden layer outputs to itself. The RNN model is commonly used for time-series-related work, particularly in predicting climate variables. It can analyze input and output data from other identification models to make accurate predictions. Authors in [30] evaluated Artificial Neural Networks (ANNs), Nonlinear Autoregressive Exogenous (NARX) models, and LSTM-RNN models to predict environmental changes in temperature, humidity, and CO\({}_{2}\) in greenhouses. The best model for all data sets was the LSTM-RNN, with continuous prediction accuracy even after 30 min, obtaining an R\({}^{2}\) of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO\({}_{2}\). The work in [31] utilized five models to collect and analyze a data set from 27 greenhouses in South Korea. The variables analyzed included internal temperature, relative humidity, radiation, CO\({}_{2}\) concentration, and external temperature. The most efficient model was Bidirectional LSTM (BiLSTM), which resulted in an average R\({}^{2}\) of 0.78 and 0.81 for the pepper and tomato data sets, respectively.