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October 27, 2025Open Access

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

Key Points

Temperature field reconstruction achieved through LSTM and 3D Kriging improves accuracy under sparse-sensor setups with spatial correlations.
The method demonstrates strong predictive capabilities, achieving low RMSE of 0.45558 °C for efficient temperature estimation and monitoring.
Combining LSTM modeling and kriging techniques, the approach balances efficiency and accuracy compared to traditional methods like CFD.
Findings support digital twin and precision management innovations in controlled environment agriculture, enhancing yield stability.

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.

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