Digital twins in agriculture: A systematic literature review on modeling, semantics, and interoperability

The adoption of Digital Twins in agriculture has increased significantly in recent years, driven by advances in sensing technologies, computational modeling, and data-driven analytics. As agricultural systems become increasingly complex and interconnected, the ability to integrate heterogeneous data sources and enable consistent data exchange across platforms has become a critical requirement. However, challenges related to interoperability and data integration continue to hinder the effective deployment of agricultural Digital Twins. This paper presents a Systematic Literature Review on the application of Digital Twins in agriculture, focusing on modeling approaches, semantic representations, and interoperability mechanisms. Following a structured review protocol, studies published between 2016 and January 2026 were retrieved from major scientific digital libraries. The multi-stage screening process reduced an initial dataset of 6,968 records to a final corpus of 62 primary studies. The results show a strong concentration of research in semantic and knowledge modeling, interoperability and integration mechanisms, and analytics-driven decision support. Crop- and plant-oriented Digital Twins dominate the literature, while the digitalization of agricultural machinery and large-scale integrated farming systems remains comparatively limited. The review also identifies key research gaps related to the lack of standardized architectures, limited cross-asset integration, and scarce large-scale field validation. These findings provide a consolidated perspective on the current state of Digital Twin research in agriculture and outline future research directions toward more interoperable and scalable digital agricultural ecosystems.