Author(s)

Quanfen Liu ¹, Jingjing Wu ²

Affiliation(s)

¹ School of Computer and Information Engineering, Nantong Institute of Technology, Nantong, Jiangsu, 226000, China
² Business School, Nantong Institute of Technology, Nantong, Jiangsu, 226000, China

Corresponding Author

Jingjing Wu

ABSTRACT

The purpose of this paper is to explore how MySQL database technology can be used to process and analyze agricultural data in order to improve data management efficiency and crop yield. By analyzing existing studies and methods, this paper proposes a novel data processing scheme based on MySQL and verifies its effectiveness through experiments. In the processing speed evaluation experiment, when the word data size increased from 100000 to 5 million, the import time based on MySQL database increased from 12 seconds to 580 seconds, and the query time increased from 1.2 seconds to 72 seconds. And the query accuracy always remains above 99%. The experimental results show that this MySQL -based method can effectively improve the speed and accuracy of processing large-scale agricultural data. In addition, we also hope to apply this method to more types of agricultural data processing scenarios to test its adaptability and practicality in different situations.

KEYWORDS

Agricultural Data Processing, MySQL Database, Large-Scale Data, Query Optimization

CITE THIS PAPER

Quanfen Liu, Jingjing Wu, Research on Agricultural Data Processing Based on MySQL. Agricultural & Forestry Economics and Management (2024) Vol. 7: 18-25. DOI: http://dx.doi.org/10.23977/agrfem.2024.070203.

REFERENCES

[1] Zhao Chunjiang. A review of agricultural knowledge intelligent service technologies[J]. Intelligent Agriculture in English, 2023, 5(2):126-148.
[2] Cravero A, Pardo S, Sepúlveda S, et al. Challenges to use machine learning in agricultural big data: a systematic literature review[J]. Agronomy, 2022, 12(3): 748-761.
[3] Zhao E, Sun S, Wang S. New developments in wind energy forecasting with artificial intelligence and big data: A scientometric insight[J]. Data Science and Management, 2022, 5(2): 84-95.
[4] Nasirahmadi A, Hensel O. Toward the next generation of digitalization in agriculture based on digital twin paradigm[J]. Sensors, 2022, 22(2): 498-512.
[5] Debauche O, Mahmoudi S, Manneback P, et al. Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(9): 7494-7514.
[6] Khan A, Hassan M, Shahriyar A K. Optimizing onion crop management: A smart agriculture framework with iot sensors and cloud technology[J]. Applied Research in Artificial Intelligence and Cloud Computing, 2023, 6(1): 49-67.
[7] Chang J H, Chiu P S, Lai C F. Implementation and evaluation of cloud-based e-learning in agricultural course[J]. Interactive Learning Environments, 2023, 31(2): 908-923.
[8] Akhter R, Sofi S A. Precision agriculture using IoT data analytics and machine learning[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(8): 5602-5618.
[9] Nie J, Wang Y, Li Y, et al. Sustainable computing in smart agriculture: survey and challenges[J]. Turkish Journal of Agriculture and Forestry, 2022, 46(4): 550-566.
[10] Fei S, Hassan M A, Xiao Y, et al. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat[J]. Precision agriculture, 2023, 24(1): 187-212.
[11] Upadhyaya A, Singh A K, Kumar S, et al. Development of relational data base management information system on integrated farming: Data base information system for integrated farming[J]. Journal of AgriSearch, 2022, 9(4): 342-346.
[12] Ngo V M, Duong T V T, Nguyen T B T, et al. A big data smart agricultural system: recommending optimum fertilisers for crops[J]. International Journal of Information Technology, 2023, 15(1): 249-265.
[13] Anass D, Madi A A, Alihamidi I, et al. A novel autonomous remote system applied in agriculture using transmission control protocol[J]. Int J Reconfigurable & Embedded Syst, 2022, 11(1): 1-12. 
[14] Wu J, Pichler D, Marley D, Wilson D, Hovakimyan N, & Hobbs J. Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis. arXiv preprint arXiv:2023, 2303:2460.

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Financial Engineering and Risk Management
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics