Production Data Management of Smart Farming Based on Shili Theory

Shuyao Li, Wenfu Wu (), Yujia Wang, Na Zhang, Fanhui Sun, Feng Jiang and Xiaoshuai Wei
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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
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

Agriculture, 2023, vol. 13, issue 4, 1-26

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.

Keywords: smart farming; systematic thinking; Shili Theory; event; natural regularity; data management (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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