Mapping Global Research on Precision Agriculture with a Bibliometric Approach Analysis Period 2010–2025
DOI:
https://doi.org/10.58812/wsa.v4i01.2676Keywords:
Precision Agriculture, Bibliometric Analysis, Machine Learning, Smart Agriculture, Sustainable FarmingAbstract
Precision Agriculture (PA) has emerged as a transformative approach in modern farming, integrating advanced digital technologies to enhance productivity, resource efficiency, and sustainability. This study aims to map global research trends in Precision Agriculture using a bibliometric analysis covering the period 2010–2025. Bibliographic data were retrieved from the Scopus database and analyzed using VOSviewer to examine publication growth, citation patterns, authorship collaboration, institutional networks, country contributions, and keyword co-occurrence structures. The results indicate a significant and continuous increase in scholarly output, reflecting the growing strategic importance of PA in addressing global food security and climate challenges. China, the United States, and India emerge as leading contributors, supported by strong institutional collaborations and expanding international research networks. Thematic analysis reveals that artificial intelligence, machine learning, deep learning, UAV-based remote sensing, IoT systems, and sustainability-related topics dominate the research landscape. The findings highlight a clear shift toward data-driven agricultural systems and integrated smart farming ecosystems. This study provides a comprehensive overview of the intellectual, social, and conceptual structure of Precision Agriculture research and offers insights for future research directions, technological innovation, and policy development to support sustainable agricultural transformation
References
[1] E. Bwambale, F. K. Abagale, and G. K. Anornu, “Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review,” Agric. Water Manag., vol. 260, p. 107324, 2022.
[2] E. Ofori, T. Griffin, and E. Yeager, “Duration analyses of precision agriculture technology adoption: what’s influencing farmers’ time-to-adoption decisions?,” Agric. Financ. Rev., vol. 80, no. 5, pp. 647–664, 2020.
[3] Y. S. Tey and M. Brindal, “Factors influencing the adoption of precision agricultural technologies: a review for policy implications,” Precis. Agric., vol. 13, pp. 713–730, 2012.
[4] D. John, N. Hussin, M. S. Shahibi, M. Ahmad, H. Hashim, and D. S. Ametefe, “A systematic review on the factors governing precision agriculture adoption among small-scale farmers,” Outlook Agric., vol. 52, no. 4, pp. 469–485, 2023.
[5] A. N. Cambouris, B. J. Zebarth, N. Ziadi, and I. Perron, “Precision agriculture in potato production,” Potato Res., vol. 57, pp. 249–262, 2014.
[6] X. Zhang, E. A. Davidson, D. L. Mauzerall, T. D. Searchinger, P. Dumas, and Y. Shen, “Managing nitrogen for sustainable development,” Nature, vol. 528, no. 7580, pp. 51–59, 2015.
[7] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018.
[8] H. Shakhatreh et al., “Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,” IEEE access, vol. 7, pp. 48572–48634, 2019.
[9] C. Zhang and J. M. Kovacs, “The application of small unmanned aerial systems for precision agriculture: a review,” Precis. Agric., vol. 13, pp. 693–712, 2012.
[10] D. J. Mulla, “Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps,” Biosyst. Eng., vol. 114, no. 4, pp. 358–371, 2013.
[11] M. Weiss, F. Jacob, and G. Duveiller, “Remote sensing for agricultural applications: A meta-review,” Remote Sens. Environ., vol. 236, p. 111402, 2020.
[12] K. Chen, Y. Wang, R. Zhang, H. Zhang, and C. Gao, “CRISPR/Cas genome editing and precision plant breeding in agriculture,” Annu. Rev. Plant Biol., vol. 70, no. 1, pp. 667–697, 2019.
[13] R. Gebbers and V. I. Adamchuk, “Precision agriculture and food security,” Science (80-. )., vol. 327, no. 5967, pp. 828–831, 2010.
[14] A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review,” Comput. Electron. Agric., vol. 151, pp. 61–69, 2018.
[15] R. P. Sishodia, R. L. Ray, and S. K. Singh, “Applications of remote sensing in precision agriculture: A review,” Remote Sens., vol. 12, no. 19, p. 3136, 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Loso Judijanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
westscience.journals@gmail.com