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Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

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  • Jingyi Zhang

    (College of Economy and Management, Northwest A&F University, Xianyang 712100, China)

  • Jiaxin Liu

    (College of Economy and Management, Northwest A&F University, Xianyang 712100, China)

  • Yaqi Chen

    (College of Economy and Management, Northwest A&F University, Xianyang 712100, China)

  • Xiaochun Feng

    (College of Economy and Management, Northwest A&F University, Xianyang 712100, China)

  • Zilai Sun

    (College of Economy and Management, Northwest A&F University, Xianyang 712100, China)

Abstract

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.

Suggested Citation

  • Jingyi Zhang & Jiaxin Liu & Yaqi Chen & Xiaochun Feng & Zilai Sun, 2021. "Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7662-:d:591068
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    References listed on IDEAS

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    1. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    2. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    3. Cui, Ningbo & Du, Taisheng & Kang, Shaozhong & Li, Fusheng & Zhang, Jianhua & Wang, Mixia & Li, Zhijun, 2008. "Regulated deficit irrigation improved fruit quality and water use efficiency of pear-jujube trees," Agricultural Water Management, Elsevier, vol. 95(4), pages 489-497, April.
    4. Kamble, Sachin S. & Gunasekaran, Angappa & Gawankar, Shradha A., 2020. "Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications," International Journal of Production Economics, Elsevier, vol. 219(C), pages 179-194.
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    Cited by:

    1. Chun Wai Fan & Jiayi Lin & Barry Lee Reynolds, 2023. "A Bibliometric Analysis of Trending Mobile Teaching and Learning Research from the Social Sciences," Sustainability, MDPI, vol. 15(7), pages 1-15, April.

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