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A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments

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  • Qingchuan Zhang

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Zihan Li

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Wei Dong

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Siwei Wei

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Yingjie Liu

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Min Zuo

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models.

Suggested Citation

  • Qingchuan Zhang & Zihan Li & Wei Dong & Siwei Wei & Yingjie Liu & Min Zuo, 2023. "A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4120-:d:1080202
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    References listed on IDEAS

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