Research on the Prediction of Greenhouse Temperature and Humidity Using an IPSO-LSTM Model

This paper presents a study on the prediction of greenhouse temperature and humidity using an improved particle swarm optimization (IPSO) algorithm combined with a long short-term memory (LSTM) neural network, namely IPSO-LSTM. Accurate environmental prediction is crucial for modern protected agriculture, but traditional LSTM models often suffer from suboptimal hyperparameter tuning. To address this, we propose an IPSO-LSTM model where an improved PSO with a linearly decreasing inertia weight is employed to automatically search for the optimal hyperparameters of the LSTM. Experimental results based on hourly data collected from a Venlo-type glass greenhouse over a 90-day period demonstrate the superiority of the proposed model. Specifically, in the tomato scenario, the IPSO-LSTM achieved an R2 of 0.9969 for temperature and 0.9967 for humidity, with MAPE values as low as 0.0118 and 0.0127, respectively. These results indicate that the IPSO-LSTM model significantly outperforms conventional LSTM and standard PSO-LSTM models, providing a reliable tool for intelligent greenhouse climate control.