  Temperature fluctuations significantly affect microorganism growth and pest
activities in grain pile, precise monitoring and forecasting temperature of
stored grain are essential for maintaining the quality and safety of grain
storage. This paper proposes a multi-model fusion approach to predict grain
temperature using historical temperature data of stored grain and
meteorological data from the region. Firstly, four distinct machine learning
models, namely Adaboost, decision tree, extra trees, and random forest, are
fine-tuned through parameter optimization to enhance their predictive
capabilities respectively; Subsequently, these optimized models are fused to
form different ensemble models, which are compared for predidction accuracy to
obtain the optimal fusion model. In essence, the fusion process integrates the
predictions of each individual model as new feature inputs into the fusion
models. Furthermore, random forest is utilized to identify the key factors
influencing grain temperature, providing insights into the importance of
different influencing factors. The experimental results demonstrate that the
proposed fusion models can achieve higher prediction accuracy and robustness
compared with the single-model prediction methods. Additionally, the analysis
of feature importance also offers empirical evidence for understanding the
factors influencing grain temperature.
