Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse

Abstract Greenhouses provide controlled environmental conditions for crop cultivation but require careful management to ensure ideal growing conditions. In this study, we tested three deep-learning-based neural network models (Artificial neural network, ANN; Nonlinear autoregressive exogenous model, NARX; and Recurrent neural networks – Long short-term memory, RNN-LSTM) to determine the best approach to predicting environmental changes in temperature, humidity, and CO2 within a greenhouse to improve management strategies. This study determined the prediction performance for time steps from 5 to 30 min and showed that the accuracy of the time-based algorithm gradually decreased as prediction time increased. The best model for all datasets was RNN-LSTM, even after 30 min, with an R2 of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO2 concentration. The results of this study show that it is possible to apply deep-learning-based prediction models for more precisely managing greenhouse.