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Progress and Prospective in the Development of Stored Grain Ecosystems in China: From Composition, Structure, and Smart Construction to Wisdom Methodology

Author

Listed:
  • Yunshandan Wu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
    These authors contributed equally to this work.)

  • Wenfu Wu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
    School of Grain Science and Technology, Jilin Business and Technology College, Changchun 130507, China
    These authors contributed equally to this work.)

  • Kai Chen

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Ji Zhang

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Zhe Liu

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Yaqiu Zhang

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

Abstract

Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices.

Suggested Citation

  • Yunshandan Wu & Wenfu Wu & Kai Chen & Ji Zhang & Zhe Liu & Yaqiu Zhang, 2023. "Progress and Prospective in the Development of Stored Grain Ecosystems in China: From Composition, Structure, and Smart Construction to Wisdom Methodology," Agriculture, MDPI, vol. 13(9), pages 1-13, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1724-:d:1229613
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    References listed on IDEAS

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    1. F. Hammami & S. Ben Mabrouk & A. Mami, 2016. "Modelling and simulation of heat exchange and moisture content in a cereal storage silo," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 22(3), pages 207-220, May.
    2. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    3. Shuyao Li & Wenfu Wu & Yujia Wang & Na Zhang & Fanhui Sun & Feng Jiang & Xiaoshuai Wei, 2023. "Production Data Management of Smart Farming Based on Shili Theory," Agriculture, MDPI, vol. 13(4), pages 1-26, March.
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