ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction

Wei Zhou (), Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li and Chang Xie
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Wei Zhou: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Shuo Liu: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Junxian Guo: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Na Liu: Agricultural Equipment Research Institute, Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences, Urumqi 830052, China
Zhenglin Li: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Chang Xie: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 8, 1-20

Abstract: Accurate prediction of greenhouse temperatures is essential for developing effective environmental control strategies, as the precision of minimum temperature data acquisition significantly impacts the reliability of predictive models. Traditional monitoring methods face inherent challenges due to the conflicting demands of temperature-field uniformity assumptions and the costs associated with sensor deployment. This study introduces an ARIMA-Kriging spatiotemporal coupling model, which combines temperature time-series data with sensor spatial coordinates to accurately determine minimum temperatures in greenhouses while reducing hardware costs. Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R 2 ) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. Multi-weather validation confirmed the model’s robustness under rainy, snowy, and overcast conditions, maintaining R 2 ≥ 0.95. Optimal prediction accuracy was observed in clear weather (RMSE = 0.71 °C), whereas rainy/snowy conditions showed a 42.9% improvement in MAPE compared to the PSO-BP model. This study provides reliable decision-making support for precise environmental regulation in facility greenhouse environments, effectively advancing the intelligent development of agricultural environmental control systems.

Keywords: greenhouse; ARIMA-Kriging model; temperature identification; GWO-BiLSTM; temperature prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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