Temperature Prediction of Mushrooms Based on a Data—Physics Hybrid Approach

Abstract

Temperature has a significant impact on the production of edible mushrooms. The industrial production of edible mushrooms is committed to accurately maintaining the temperature inside the mushroom room within a certain range to achieve quality and efficiency improvement. However, current environmental regulation methods have problems such as lagging regulation and a large range of temperature fluctuations. There is an urgent need to accurately predict the temperature of mushroom houses in the future period to take measures in advance. Therefore, this article proposes a temperature prediction model for mushroom houses using a data–physical hybrid method. Firstly, the Boruta-SHAP algorithm was used to screen out the key influencing factors on the temperature of the mushroom room. Subsequently, the indoor temperature was decomposed using the optimized variational modal decomposition. Then, the gated recurrent unit neural network and attention mechanism were used to predict each modal component, and the mushroom house heat balance equation was incorporated into the model’s loss function. Finally, the predicted values of each component were accumulated to obtain the final result. The results demonstrated that integrating a simplified physical model into the predictive model based on data decomposition led to a 12.50% reduction in the RMSE of the model’s predictions compared to a purely data-driven model. The model proposed in this article exhibited good predictive performance in small datasets, reducing the time required for data collection in modeling.

Keywords

• data-physics hybrid
• VMD
• prediction
• mushroom room