Date of this Version
- Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: a patient-centered framework. Journal of general internal medicine, 28(3), 660-665.
- Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
- Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 2053951716648174.
- Tang, M., Liao, H., Wan, Z., Herrera-Viedma, E., & Rosen, M. (2018). Ten years of sustainability (2009 to 2018): A bibliometric overview. Sustainability, 10(5), 1655.
- Liao, H., Tang, M., Luo, L., Li, C., Chiclana, F., & Zeng, X. J. (2018). A bibliometric analysis and visualization of medical big data research. Sustainability, 10(1), 166.
- Moed, H., De Bruin, R., & Van Leeuwen, T. H. (1995). New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics, 33(3), 381-422.
- Liu, W., & Liao, H. (2017). A bibliometric analysis of fuzzy decision research during 1970–2015. International Journal of Fuzzy Systems, 19(1), 1-14.
- Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-Based Systems, 80, 3-13.
- Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420-433.
- Barr, B. (2015, Sep 30). Big Data: 20 Mind-Boggling Facts Everyone Must Read. Retrieved from https://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/.
- Shekhar, S., Schnable, P., LeBauer, D., Baylis, K., & VanderWaal, K. (2017). Agriculture Big Data (AgBD) Challenges and Opportunities From Farm To Table: A Midwest Big Data Hub Community Whitepaper.
- van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
- Du, H., Li, N., Brown, M. A., Peng, Y., & Shuai, Y. (2014). A bibliographic analysis of recent solar energy literatures: The expansion and evolution of a research field. Renewable Energy, 66, 696-706.
- Pritchard, A., & Wittig, G. R. (1981). Bibliometrics. AllM Books.
- Hood, W., & Wilson, C. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291-314.
- Carbonell, I. (2016). The ethics of big data in big agriculture. Internet Policy Review, 5(1).
- Sonka, S. (2014). Big data and the ag sector: More than lots of numbers. International Food and Agribusiness Management Review, 17(1030-2016-82967), 1.
- Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
- Tsay, M. Y. (2008). A bibliometric analysis of hydrogen energy literature, 1965–2005. Scientometrics, 75(3), 421-438.
The present paper is a scientometric and visualization study of global Agriculture Big Data (ABD) research. however as per the quantification is concern, very little research have performed in the area of Agriculture Big Data. The study aims to traverse the present status of Agriculture Big Data research through network analysis and visualizations study of the ABD publications. A total of 379 publication data were downloaded from Clarivate Analytics Web of Science database within the time span of "all years". VOSviewer, MS-Excel and R statistical software is used for data analysis. Various results are drawn based on annual scientific production, most cited papers, most cited authors, most cited affiliations, most prolific nations, author's h-index, co-authorship analysis of countries and organizations, co-citation analysis of sources, the keyword co-occurrence analysis and density visualization. This study investigates the growth status in ABD related research which can help the policy maker, researchers and people of agriculture and allied sector to have an exhaustive understanding on Agriculture Big Data research for the further study.