Power-LSTM for smart greenhouse: a novel deep learning approach to temperature prediction in a Mexican case study

Abstract

This paper addresses the challenge of predicting internal temperature in green-house environments, a critical aspect of optimizing crop growth and ensuring resource efficiency. While machine learning (ML) techniques have been widely applied to predict greenhouse climates, deep learning (DL) methods offer the po-tential to capture more complex relationships within the data. In this study, we present a comprehensive evaluation of ML and DL models, along with our pro-posed power-long short-term memory (PLSTM) model, to predict the internal temperature of a greenhouse using a database from Mexico. We compared tradi-tional ML models such as linear regression (LR) and extreme gradient boosting (XGBoost) with DL architectures like gated recurrent unit (GRU), artificial neu-ral networks (ANN), hybrid LSTM-ANN and LSTM-RNN architectures. Our proposed PLSTM model outperformed both ML and DL models, achieving the R2 score of 0.9710, and root mean square error (RMSE) equal to 0.1710, high-lighting its superior ability to predict complex time-series data.