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

Jingyi Zhang; Jiaxin Liu; Yaqi Chen; Xiaochun Feng; Zilai Sun

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

2021

Jingyi Zhang | Jiaxin Liu | Yaqi Chen | Xiaochun Feng | Zilai Sun

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.

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