BIBLIOMETRIC STUDY: MACHINE LEARNING APPLIED IN AGRICULTURE

Authors

  • Gizele Ferreira da Silva
  • David Lopes Maciel
  • Carlos Alberto Paraguassu-Chaves
  • Fabrício Moraes de Almeida

Abstract

The statistical analysis of bibliographic information is the basis for bibliometric studies and the conception of models or laws that deal with the development of knowledge in the state of the art. In the 19th century, the first more systematic expression appears in an incipient way, however only in the beginning of the 20th century, with the publication of Lotka's works, does it gain strength with the insertion of production indicators [9]. Bibliometrics makes use of mathematical methods in order to describe and quantify studies related to a scientific theme [10]. In this context, the objective of this paper is to quantify the publications in the area of Machine Learning applied in agriculture, through a bibliometric analysis. For that, we used the database obtained from Scopus and Web of Science. The analysis process made use of the R / RStudio software and the Bibliometrix application and its Biblioshiny library, from the data, it was verified that the largest number of publications on the subject occurred in the years 2021 and 2022, the author who most published was the WANG Y, the most relevant journals were COMPUTERS AND ELECTRONICS IN AGRICULTURE and REMOTE SENSING.

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Published

2023-11-05

How to Cite

Gizele Ferreira da Silva, David Lopes Maciel, Carlos Alberto Paraguassu-Chaves, & Fabrício Moraes de Almeida. (2023). BIBLIOMETRIC STUDY: MACHINE LEARNING APPLIED IN AGRICULTURE. InterSciencePlace, 18(3). Retrieved from http://www.interscienceplace.org/index.php/isp/article/view/612

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