International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Quantitative Analysis on Machine Learning and Smart Farm with Bibliographic Data from 2013 to 2023

  • Yong Sauk Hau (Department of Business Administration, Yeungnam University)
  • Received : 2024.07.25
  • Accepted : 2024.08.06
  • Published : 2024.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

Keywords

References

  1. P. P. Shinde and S. Shah, "A review of machine learning and deep learning applications", Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, pp. 1-6, 2018. DOI: https//doi.org.10.1109/ICCUBEA.2018.8697857
  2. N. Manakitsa, G. S. Maraslidis, L. Moysis, and G. F. Fragulis, "A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision", Technologies, Vol.12, No. 2, 2024. DOI: https://doi.org/10.3390/technologies12020015
  3. E. Koutridi and O. Christopoulou, "The importance of integrating Smart Farming Technologies into Rural Policies (Aiming at sustainable rural development)-Stakeholders' views", Smart Agricultural Technology, Vol. 4, 2023. DOI: https://doi.org/10.1016/j.atech.2023.100206
  4. E. S. Mohamed, A. A. Belal, , S. K. Abd-Elmabod, , M. A. El-Shirbeny, A. Gad, and M. B. Zahran, "Smart farming for improving agricultural management", The Egyptian Journal of Remote Sensing and Space Science, Vol. 24, No.3, pp. 971-981, 2021. DOI: https://doi.org/10.1016/j.ejrs.2021.08.007
  5. N.E. Benti, M. D. Chaka, and A.G. Semie, "Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects", Sustainability, Vol. 15, 2023. DOI: https://doi.org/10.3390/su15097087
  6. S. K. S. Durai and M. D. Shamili, "Smart farming using machine learning and deep learning techniques", Decision Analytics Journal, Vol. 3, 2022. DOI: https://doi.org/10.1016/j.dajour.2022.100041
  7. Y. Akkem, S. K. Biswas, and A. Varanasi, "Smart farming using artificial intelligence: A review", Engineering Applications of Artificial Intelligence, Vol. 120, 2023. DOI: https://doi.org/10.1016/j.engappai.2023.105899
  8. L. Zhang, J. Ling, and M. Lin, "Artificial intelligence in renewable energy: A comprehensive bibliometric analysis", Energy Reports, Vol. 8, pp. 14072-14088. 2022. DOI: https://doi.org/10.1016/j.egyr.2022.10.34
  9. K. Zhang and Q. Liang, "Recent progress of cooperation on climate mitigation: A bibliometric analysis", Journal of Cleaner Production, Vol. 277, 2020. DOI: https://doi.org/10.1016/j.jclepro.2020.123495
  10. M. Aria and C. Cuccurullo, "bibliometrix: An R-tool for comprehensive science mapping analysis", Journal of Informetrics, Vol. 11, No. 4, pp. 959-975, 2017. DOI: https://doi.org/10.1016/j.joi.2017.08.007
  11. bibliometrix.org, BIBLIOMETRIX, https://www.bibliometrix.org/home.