Digital Twins in Agriculture: A Comprehensive Bibliometric Exploration of Applications and Future Trends

Abstract

This bibliometric study was conducted between September and December, 2025 using data retrieved from the Scopus database, focusing on global research publications related to Digital Twin applications in agriculture. Digital Twin technology has become a key component of Agriculture 4.0, enabling real-time simulation, prediction, and optimization of farming systems through virtual representations of physical environments. As agriculture faces rising food demand, climate variability, labour shortages, and resource constraints, Digital Twins offer significant potential to improve productivity, sustainability, and decision-making. However, research in this area remains fragmented across domains such as IoT, AI, robotics, precision agriculture, and climate-smart practices. Using a structured search strategy and PRISMA screening, 95 eligible articles published between 2019 and 2026 were identified. Bibliometric analysis was performed using the Bibliometrix R package and Biblioshiny interface, covering descriptive indicators, co-authorship and co-citation networks, keyword co-occurrence, and thematic evolution. Results show a fluctuating yet overall increasing publication trend, with a notable rise in 2025. Major publication sources include IEEE Access, Applied Sciences, Sensors, and Smart Agricultural Technology. China, the Netherlands, and the USA are leading contributors, with the Netherlands demonstrating the highest citation impact. Keyword analysis highlights strong emphasis on Digital Twins, IoT, machine learning, embedded systems, and agricultural robotics. Thematic mapping identifies core motor themes and emerging areas such as smart agriculture and climate resilience. Overall, the study consolidates fragmented knowledge, identifies research gaps, and outlines future directions to advance Digital Twin applications for sustainable, data-driven agriculture.