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

Mingfei Wang, Xiangshu Kong, Feifei Shan, Wengang Zheng, Pengfei Ren, Jiaoling Wang, Chunling Chen, Xin Zhang () and Chunjiang Zhao ()
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Mingfei Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiangshu Kong: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Feifei Shan: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Wengang Zheng: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Pengfei Ren: Institute of Agricultural Resources and Environment, Shangdong Academy of Agricultural Sciences, Jinan 250100, China
Jiaoling Wang: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affais, Nanjing 210014, China
Chunling Chen: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xin Zhang: Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Chunjiang Zhao: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2024, vol. 14, issue 1, 1-16

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 (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: 2024
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