Discovery of knowledge on digital agriculture: research productivity, current scientific outcome and future research directions

Purpose This paper aims to analyse the productivity of scientific research and the discovery of knowledge on digital agriculture globally. Design/methodology/approach This study is based on a large data set of 5,617 articles from the Scopus database published from 1980 to 2024. The data were analysed to map research productivity, knowledge discovery and future research themes using RStudio and VOSViewer software. Findings The scientific research on digital agriculture shows a significant annual growth rate of 14.8 % during 1980–2024. Furthermore, the discovery of knowledge has been clustered based on the author’s keywords. Finally, the evolution of thematic keywords indicates that the use of remote sensing, geographical information systems and image processing were major themes during the first phase, i.e. 1980–2009, which moved to precision agriculture and digital agriculture during 2010–22 and machine learning, deep learning, the Internet of Things, smart farming and the digital economy in recent years. Practical implications This research offers interesting insights into how scientific understanding of digital agriculture has shifted from basic digital technologies to advanced applications, and provides a way forward for future research to strengthen the adoption of digital technologies in agriculture. Originality/value This paper synthesises knowledge on digital agriculture, maps the phases of development of digitisation and agriculture and provides directions for future research.