Fusion of LSTM-Based Vertical-Gradient Prediction and 3D Kriging for Greenhouse Temperature Field Reconstruction

Zhimin Zhang, Xifeng Liu, Xiaona Zhao, Zihao Gao, Yaoyu Li, Xiongwei He, Xinping Fan, Lingzhi Li and Wuping Zhang ()
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Zhimin Zhang: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Xifeng Liu: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Xiaona Zhao: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Zihao Gao: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Yaoyu Li: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Xiongwei He: College of Software, Shanxi Agricultural University, Jinzhong 030801, China
Xinping Fan: Shanxi Academy of Agricultural Sciences, Pomology Institute, Jinzhong 030815, China
Lingzhi Li: College of Horticultural, Shanxi Agricultural University, Jinzhong 030801, China
Wuping Zhang: College of Software, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2025, vol. 15, issue 21, 1-19

Abstract: This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) ( p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA.

Keywords: greenhouse microclimate; 3D temperature field; long short-term memory (LSTM); vertical temperature gradient; 3D ordinary kriging interpolation (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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