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

文献类型： 外文期刊

作者： Zhang, Xin 1 ; Zeng, Xinwen 1 ; Wei, Yibo 3 ; Zheng, Wengang 3 ; Wang, Mingfei 3 ;

作者机构： 1.Xinjiang Agr Univ, Coll Mech & Elect Engn, Urumqi 830052, Peoples R China

2.Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： shiitake mushroom stick; acoustic thermometry; data-physics hybrid; non-destructive measurement

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： 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 degrees 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 similar to 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.

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