Long Short-Term Memory Recurrent Neural Network and Extreme Gradient Boosting Algorithms Applied in a Greenhouse’s Internal Temperature Prediction

Abstract

One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse’s internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R2, RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year’s four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R2 = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of 2 C hysteresis.