Journal of Agricultural Big Data ›› 2019, Vol. 1 ›› Issue (2): 64-75.doi: 10.19788/j.issn.2096-6369.190206

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Bibliometrics-Based Analysis of Advances in Plant Phenomics Research

Xiaoman Li1,Yang Zhang2,Qian Xu1,Nengfu Xie1,*()   

  1. 1.Agricultural Information Institute of the Chinese Academy of Agricultural Sciences, Beijing 100081
    2.Cash Crop Research Institute of Henan Academy of Agricultural Sciences,Zhengzhou 450002
  • Received:2019-04-26 Online:2019-06-26 Published:2019-08-21
  • Contact: Nengfu Xie E-mail:xienengfu@caas.cn

Abstract:

[Objective] To understand the development and current state of plant phenomics research, we performed a bibliometrics-based analysis. [Methods] We reviewed entries in the plant phenomics research domain in the Web of Science core collection database from 1995 to 2018 using multiple indicators, such as academic output (e.g. annual trends, countries, journals, and subject areas) and academic collaborations (i.e. national and institutional cooperation). We used the Web of Science database document analysis platform, Excel, DDA software, and a word co-occurrence analysis method and visualized the results with the VOSviewer bibliometric tool. We analyzed changes in research topics during three time periods: 1995 to 2002, 2003 to 2010, and 2011 to 2018. [Results] Based on Web of Science, a total of 6,800 plant phenomics articles, both basic research and applications, have been published. The number of plant phenomics publications is increasing, with an accelerating trend in recent years. In terms of academic output by country, the United States is clearly in the lead, whereas China presently ranks fourth in the world. Papers published by Canadian, Spanish and Italian authors include the largest number of international partnerships, while articles from the United States comprise 37.28% of international collaborations. Although multidisciplinary studies are increasing in the plant phenomics field, plant science is still the main focus of this research domain. Research topics include the application of remote sensing technologies, theoretical issues, time-series imaging analysis techniques, machine learning, computer vision, and research using plant species such as wheat and rice.

Key words: bibliometrics, plant phenomics, co-occurrence analysis, remote sensing technology, machine learning, computer vision, data mining, data acquisition

CLC Number: 

  • S-1