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Production Data Management of Smart Farming Based on Shili Theory

Author

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  • Shuyao Li

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

  • 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)

  • Yujia Wang

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

  • Na Zhang

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

  • Fanhui Sun

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

  • Feng Jiang

    (School of Management, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Xiaoshuai Wei

    (School of Economics and Management, North China Institute of Science and Technology, Langfang 131000, China)

Abstract

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:751-:d:1105785
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    References listed on IDEAS

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    1. Dimitrios Kalfas & Stavros Kalogiannidis & Olympia Papaevangelou & Katerina Melfou & Fotios Chatzitheodoridis, 2024. "Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    2. 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.

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