## 2 Related Work  
This literature review examines current farming systems and how they can be improved by combining data from various sources. It provides valuable insights into the latest developments in agriculture technology, focusing on ML applications in greenhouse farming.  
Research projects have tested temperature prediction in greenhouses by collecting data on variables such as temperature, humidity, and carbon dioxide levels. A system was used to collect 62 days of information from a multi-span greenhouse, in which a model with a Multilayer Perceptron Neural Network (MLP-NN) was developed to model the internal temperature and relative humidity [24]. A study tested an optimal ventilation control system to regulate the temperature of a single-span greenhouse. The researchers created a prediction model using Artificial Neural Networks (ANN) and a dataset collected over two months [25]. In contrast, in another work with the same method, they work to improve the accuracy of prediction algorithms in dynamic conditions but using fifteen days of data [26]. In another study, authors tested different models; ANN, Nonlinear Autoregressive Exogenous (NARX), and RNN-LSTM, to determine which was most effective in predicting changes in variables that directly impact greenhouse crop growth. They collected data over a year to conduct their analysis [27]. Also, a model with 172 days of data is used based on a Bidirectional self-attentive Encoder-Decoder framework (BEDA) and LSTM. This model predicts indoor environmental factors from noisy IoT-based sensors [28]. The methods for collecting greenhouse data vary among the studies presented, with some using sensors or meteorological stations. The frequency of data collection depends on the specific application.  
Various investigations use multiple variables, including radiation, pressure, direction, and wind speed, to predict soil temperature and water content in greenhouses. For instance, one study analyzed data collected over four months and used random forest and the inferring connections of networks to predict the soil temperature and volumetric water [29]. Another study used a Reversible Automatic Selection Normalization (RASN) network over six months of data to evaluate the prediction model using different variables [30]. ANN were also used to forecast internal temperature using around two months of information [31]. In refs. [32; 33; 34] use SVM to make predictions about certain variables in greenhouses. Other studies used algorithms such as Xgboost [35] or LSTM [36] to analyze meteorological factors affecting crop evapotranspiration. One work predicted greenhouse aerial environments using the BiLSTM model with a dataset of two years [37]. In another approach, the spatio-temporal kriging method was used to estimate the temperature in greenhouses using three months of data [38].  
Sometimes, data is not acquired directly for investigations. Instead, datasets are gathered from other projects or repositories to make AI predictions. Some approaches involve adding feature functions to time series through techniques like LR, SVR, RMM, or LSTM for predictive analysis [39; 40]. Additionally, certain studies employ methods like the Bayesian optimization-based multi-head attention encoder to forecast changes in climate time series accurately [41].  
Each study evaluates its model's performance using metrics such as coefficient of determination R-squared (R\({}^{2}\)), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The percent Standard Error of the Prediction (%SEP), Symmetric Mean Absolute Percentage Error (SMAPE), and Pearson Correlation Coefficient (R) are commonly used.