Integrated digital twins for agricultural machinery and robotics: A systematic review of architectures, quantified impacts, and deployment gaps
- 1Department of Agricultural and Biosystems Engineering, Fargo, N.D. 58102
- 2NDSU Peltier Institute for the Advancement of Agricultural Technology, North Dakota State University, Fargo, ND 58108-6050, USA
Abstract
Digital twin (DT) technology is progressively employed to facilitate autonomous field operations, predictive maintenance, and adaptive control in precision agriculture. This review analyzes 443 sources published from 2015 to 2026. The literature was categorized into 322 peer-reviewed studies, 32 scholarly preprints, and 89 non-peer-reviewed sources, comprising trade articles, commercial reports, and technical blogs, to uphold a clear evidence base. From the core corpus, 32 papers were chosen for comprehensive structural analysis, while the other studies were utilized to figure out overall trends in simulation platforms, modeling methodologies, and robotic applications. The review establishes a classification framework for agricultural digital twin systems, categorizing them according to physical and virtual twin structures and four levels of integration. Recent research demonstrates significant advancements in CAD-based modeling, photogrammetry, model predictive control, edge computing, and single-machine navigation. However, several significant gaps persist. Many systems continue to represent machinery and field environments at a rudimentary scale, with insufficient focus on crop interaction, nonlinear soil-tool dynamics, and internal machine degradation, including drivetrain fatigue. The shift from single-machine systems to fleet-level deployment is constrained by inconsistent communication protocols, fragmented data pipelines, and unreliable rural network connectivity. The analysis indicates that practical implementation relies on factors beyond mere technical performance. Limited compatibility with older machinery, high initial cost, uncertain data ownership, and the absence of certification procedures for autonomous field decisions continue to prevent deployment. Future studies should advance beyond individual prototypes to develop interoperable, closed-loop digital twin systems validated in actual farming environments throughout various seasons.
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BibTeX
@article{lokavarapu2026integrated,
title = {{Integrated digital twins for agricultural machinery and robotics: A systematic review of architectures, quantified impacts, and deployment gaps}},
author = {Navya Tejaswini Lokavarapu and Xin Sun},
journal = {Journal of Agriculture and Food Research},
year = {2026},
month = jun,
doi = {10.1016/j.jafr.2026.103051},
url = {https://doi.org/10.1016/j.jafr.2026.103051},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Integrated digital twins for agricultural machinery and robotics: A systematic review of architectures, quantified impacts, and deployment gaps
AU - Navya Tejaswini Lokavarapu
AU - Xin Sun
PY - 2026
DA - 2026/06/01
JO - Journal of Agriculture and Food Research
DO - 10.1016/j.jafr.2026.103051
UR - https://doi.org/10.1016/j.jafr.2026.103051
AB - Digital twin (DT) technology is progressively employed to facilitate autonomous field operations, predictive maintenance, and adaptive control in precision agriculture. This review analyzes 443 sources published from 2015 to 2026. The literature was categorized into 322 peer-reviewed studies, 32 scholarly preprints, and 89 non-peer-reviewed sources, comprising trade articles, commercial reports, and technical blogs, to uphold a clear evidence base. From the core corpus, 32 papers were chosen for comprehensive structural analysis, while the other studies were utilized to figure out overall trends in simulation platforms, modeling methodologies, and robotic applications. The review establishes a classification framework for agricultural digital twin systems, categorizing them according to physical and virtual twin structures and four levels of integration. Recent research demonstrates significant advancements in CAD-based modeling, photogrammetry, model predictive control, edge computing, and single-machine navigation. However, several significant gaps persist. Many systems continue to represent machinery and field environments at a rudimentary scale, with insufficient focus on crop interaction, nonlinear soil-tool dynamics, and internal machine degradation, including drivetrain fatigue. The shift from single-machine systems to fleet-level deployment is constrained by inconsistent communication protocols, fragmented data pipelines, and unreliable rural network connectivity. The analysis indicates that practical implementation relies on factors beyond mere technical performance. Limited compatibility with older machinery, high initial cost, uncertain data ownership, and the absence of certification procedures for autonomous field decisions continue to prevent deployment. Future studies should advance beyond individual prototypes to develop interoperable, closed-loop digital twin systems validated in actual farming environments throughout various seasons.
SN - 2666-1543
ER -