Probabilistic Deep Learning Framework for Greenhouse Microclimate Prediction with Time-Varying Uncertainty and Covariance Analysis

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly in strawberry greenhouse. However, existing greenhouse microclimate deterministic prediction models do not adequately reflect the nonlinear, time-varying characteristics of greenhouses and the inherent uncertainty in data, limiting probabilistic decision-making. In this study, we developed a probabilistic deep learning framework to estimate and interpret uncertainty while simultaneously predicting greenhouse microclimate quantitatively. The proposed one-dimensional convolutional neural network model learned the time-series characteristics of greenhouse internal and external environmental information and control data, predicting a total of nine parameters, including three-dimensional predicted values 3 h later and six-dimensional covariance elements. The model demonstrated high sharpness and calibration performance, with an average R2 of 0.93, a negative log likelihood of 2.08, and a Coverage 90% of 0.901 for three microclimates. In addition, the estimated covariance matrix was used to interpret the time-varying correlations between microclimate variables, confirming local simultaneous variability not captured by global correlation analysis. These results suggest that the model in this study can provide greenhouse operators with explainable uncertainty interpretation and robust control decision support information.