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Cited by 9Smart Agriculture and AIGreenhouse Technology and Climate ControlAdvanced Sensor and Control Systems

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

Open access
Wang, Mingfei, Kong, Xiangshu, Shan, Feifei, Zheng, Wengang, Ren, Pengfei, Wang, Jiaoling, Chen, Chunling, Zhang, Xin, Zhao, Chunjiang·2024·Agriculture
DOI 10.3390/agriculture14010145
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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.

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  • Hybrid models demonstrate superior accuracy and enhanced generalization capabilities compared to purely physical or data-driven approaches.

    “Although these models demonstrate superior accuracy and enhanced generalization capabilities compared to purely physical or data-driven approaches [20], their development remains challenging due to the inherent complexity of fundamental physical structures in certain air conditioning systems.”

    From Research on Energy-Saving Optimization of Mushroom Growing Control Room Based on Neural Network Model Predictive Control (2025) · cites this paper as [20]

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Metadata

Authors
Wang, M., Kong, X., Shan, F., Zheng, W., Ren, P., Wang, J., Chen, C., Zhang, X., & Zhao, C.
Affiliations
  • Shenyang Agricultural University (CN)
  • Beijing Academy of Agriculture and Forestry Sciences (CN)
  • Shandong Academy of Agricultural Sciences (CN)
  • Nanjing Institute of Agricultural Mechanization (CN)
Corresponding author
  • Zhang, Xin
  • Zhao, Chunjiang
ORCID
Venue
Agriculture
Publisher
MDPI AG
Published
2024-01-19
Volume
14
Issue
1
Pages
145
ISSN
2077-0472 (ISSN-L)
DOI
10.3390/agriculture14010145
OpenAlex
W4391029254
Cited by
9
References
4
Citations by year
2026: 2 · 2025: 6 · 2024: 1
FWCI
2.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Agriculture (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Beijing Academy of Agricultural and Forestry Sciences Science and Technology Innovation Capacity Construction Project (KJCX20230410, CARS-20, BAIC03-2023)
  • China Agriculture Research System of MOF and MARA (KJCX20230410, CARS-20, BAIC03-2023)
  • Beijing edible fungi Innovation Team (KJCX20230410, CARS-20, BAIC03-2023)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Greenhouse Technology and Climate Control
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Sensor and Control Systems
Keywords
edible mushroom cultivation, heat balance equation, variational mode decomposition, gated recurrent unit, attention mechanism, Boruta feature selection, SHAP, indoor temperature control, small datasets

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