Bibliometric synthesis of intelligent systems in agriculture

Abstract This study undertakes a bibliometric analysis of recent investigations centered on intelligent systems in agriculture, a combined quantitative and qualitative approach is adopted, which is based on Visualization of Similarities (VOS) method. We then map prominent areas of interest, evaluate international collaboration extents, and identify potential research gaps. Aligned with the United Nations’ vision, the Sustainable Development Goals (SDGs) place a spotlight on the interconnection of poverty eradication (SDG 1), zero hunger (SDG 2), and fostering innovation and infrastructure development (SDG 9). The United Nations recognizes the profound challenges posed by global food insecurity, malnutrition, and the increasing vulnerability of the food system to environmental changes. As populations grow and the demand for food surges, there is an imperative to innovate and transform our agricultural and food systems. Adopting advanced technologies, like precision agriculture, remote sensing, Internet of Things (IoT), artificial intelligence (AI), and robotics, offers a promising avenue to address these challenges. Yet, despite the profound implications of these technologies in transforming agriculture, there remains a notable need for comprehensive research in this domain. Enhancing agricultural productivity, reducing resource wastage, and championing sustainability are pressing concerns. This study undertakes a bibliometric analysis of recent investigations centered on intelligent systems in agriculture, sourcing data from the Scopus database. To comprehensively analyze this vast dataset, a combined quantitative and qualitative approach is adopted, which is based on the visualization of similarities (VOS) method. We then map prominent areas of interest, evaluate international collaboration extents, and identify potential research gaps. Our findings offer pivotal insights that can shape the trajectory of future research and development in the agricultural sector. The significance of our analysis extends beyond academic interest. The insights derived hold the potential to catalyze the growth of smart systems in agriculture, addressing global food security challenges in the process. Moreover, by championing sustainable agricultural practices via intelligent systems, our study underscores its alignment with SDGs, notably those aiming at ensuring zero hunger and promoting responsible consumption and production.