A Non-Destructive Measurement Approach for the Internal Temperature of Shiitake Mushroom Sticks Based on a Data–Physics Hybrid-Driven Model

Xin Zhang, Xinwen Zeng, Yibo Wei, Wengang Zheng and Mingfei Wang ()
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Xin Zhang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Xinwen Zeng: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yibo Wei: Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Wengang Zheng: Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Mingfei Wang: Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2024, vol. 14, issue 10, 1-21

Abstract: This study aimed to develop a non-destructive measurement method utilizing acoustic sensors for the efficient determination of the internal temperature of shiitake mushroom sticks during the cultivation period. In this research, the sound speed, air temperature, and moisture content of the mushroom sticks were employed as model inputs, while the temperature of the mushroom sticks served as the model output. A data–physics hybrid-driven model for temperature measurement based on XGBoost was constructed by integrating monotonicity constraints between the temperature of the mushroom sticks and sound speed, along with the condition that limited the difference between air temperature and stick temperature to less than 2 °C. The experimental results indicated that the optimal eigenfrequency for applying this model was 850 Hz, the optimal distance between the sound source and the shiitake mushroom sticks was 8.7 cm, and the temperature measurement accuracy was highest when the moisture content of the shiitake mushroom sticks was in the range of 56~66%. Compared to purely data-driven models, our proposed model demonstrated significant improvements in performance; specifically, RMSE, MAE, and MAPE decreased by 74.86%, 77.22%, and 69.30%, respectively, while R 2 increased by 1.86%. The introduction of physical knowledge constraints has notably enhanced key performance metrics in machine learning-based acoustic thermometry, facilitating efficient, accurate, rapid, and non-destructive measurements of internal temperatures in shiitake mushroom sticks.

Keywords: shiitake mushroom stick; acoustic thermometry; data–physics hybrid; non-destructive measurement (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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