Digital twins in agriculture: A systematic literature review on modeling, semantics, and interoperability
- 1Center for Embedded Devices and Research in Digital Agriculture (CEDRA), Sao Leopoldo, Brazil
- 2Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
Abstract
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.
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BibTeX
@article{pereira2026digital,
title = {{Digital twins in agriculture: A systematic literature review on modeling, semantics, and interoperability}},
author = {Pedro Henrique Morgan Pereira and Giovana Costa Piazza and Khalid Usman and Luan Santos Comelli da Silveira and Vinícius Cella Ceriotti and Yuri de Souza Pazin and Edison Pignaton de Freitas},
journal = {Smart Agricultural Technology},
year = {2026},
month = jun,
doi = {10.1016/j.atech.2026.102283},
url = {https://doi.org/10.1016/j.atech.2026.102283},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Digital twins in agriculture: A systematic literature review on modeling, semantics, and interoperability
AU - Pedro Henrique Morgan Pereira
AU - Giovana Costa Piazza
AU - Khalid Usman
AU - Luan Santos Comelli da Silveira
AU - Vinícius Cella Ceriotti
AU - Yuri de Souza Pazin
AU - Edison Pignaton de Freitas
PY - 2026
DA - 2026/06/12
JO - Smart Agricultural Technology
DO - 10.1016/j.atech.2026.102283
UR - https://doi.org/10.1016/j.atech.2026.102283
AB - 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.
SN - 2772-3755
ER -