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

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  • Wei Zhou

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Key Laboratory of Xinjiang Intelligent Agricultural Equipment, 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
    Key Laboratory of Xinjiang Intelligent Agricultural Equipment, 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)

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.

Suggested Citation

  • Wei Zhou & Shuo Liu & Junxian Guo & Na Liu & Zhenglin Li & Chang Xie, 2025. "ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction," Agriculture, MDPI, vol. 15(8), pages 1-20, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:900-:d:1638990
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    References listed on IDEAS

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    1. Guillaume Grégoire & Josée Fortin & Isa Ebtehaj & Hossein Bonakdari, 2023. "Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques," Agriculture, MDPI, vol. 13(6), pages 1-22, May.
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