## 2 Related Work
The model effectively adapted pre-trained deep learning models and improved their prediction ability in data-limited greenhouse microclimates. A research study described in [32] aims to improve the accuracy of predicting crop yields in greenhouses. Such precision is essential for making informed decisions about the planning and management of greenhouse agriculture. The researchers developed a new algorithm to achieve this goal by combining a Temporal Convolution Network (TCN) and an RNN. They evaluated the algorithm's performance using data from several greenhouses cultivating tomatoes. The results demonstrate that this new technique outperforms traditional machine learning methods and other deep neural networks in accuracy. The study also highlights that historical performance information is crucial for accurately predicting future crop yields. In [33], researchers have proposed a Multivariate Long Short-Term Memory (MV-LSTM) neural network model for predicting wind speed. The model is based on the feature selection of the Pearson correlation coefficient and utilizes temperature, humidity, and air pressure data to predict the wind speed at two observation stations in Beijing for the next hour. The study compared the MV-LSTM model with the Auto-Regressive Integrated Moving Average (ARIMA) and LSTM methods. It demonstrated that the MV-LSTM model outperforms the other models regarding prediction accuracy. In [34], a study was conducted to predict wind energy in ultra-short periods. The researchers developed a hybrid Spatio-Temporal Correlation Model (STCM) using Convolutional Neural Networks (CNN) and LSTM. The model takes multiple meteorological factors as input and reconstructs the data into a matrix for CNN to extract spatial correlation and LSTM for temporal correlation. The model was tested on a wind farm in China and showed a significant improvement in accuracy compared to using CNN or LSTM alone. The error rate was reduced by around 30%. However, the study recognizes the need to investigate the impact of different meteorological factors and improve the model's optimization algorithm. Research in [35] was conducted to estimate crop ET. Since ET is not easy to measure directly, a simulation model based on XGBoost Regression (XGBR) was developed to obtain it. The researchers used three years of data and eight meteorological factors (net solar radiation, mean temperature, minimum temperature, maximum temperature, relative humidity, minimum relative humidity, maximum relative humidity, and wind speed) to train the XGBo-ET model. They compared it with seven other standard regression models. The results showed that net solar radiation had the highest positive correlation with ET, while wind speed had the lowest correlation. In [36], the authors proposed a model that utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to predict the internal temperature of a greenhouse. The model uses control and temporal data collected over five years. The study reports that the LGBM model has better tuning capacity and is significantly faster in training than other models like neural networks, BP, RNN, XGBoost, and Stochastic Gradient Boosting (SGB). These results indicate that the LGBM model has great potential for real-time prediction and control of the greenhouse environment. The work in [37] uses time series data to predict ET and humidity in tomato greenhouses. The researchers applied an LSTM model to predict ET and compared it with the Stanghellini model. During the training phase, the developed ET model had an RMSE of 0.00317 and 0.00356 during the testing phase, with percentage errors of 5.76% and 6.45%, respectively. In addition, a humidity prediction model performed better than traditional LSTM-RNN models. In [38], the researchers aimed to predict the peak energy consumption of an intelligent farm using various algorithms. They analyzed energy data collected from a unique pepper farm in South Korea between 2019 and 2021.