Abstract:

[Significance] Agricultural digital twin research spans digital representation, state monitoring, prediction, decision support, and physical execution. These functions correspond to different system capabilities and verification requirements rather than to a fixed developmental sequence. Agricultural objects differ from industrial equipment: crops and animals develop continuously with individual variation, production depends on changing environmental and management conditions, and machinery and robots operate under terrain, communication, and safety constraints. These differences affect what can be observed, how models are updated, and how digital outputs enter production. This review examines the technical system, representative applications, and engineering conditions of agricultural digital twins, focusing on the transition from research prototypes to sustained operation. [Progress] The review draws primarily on the Web of Science Core Collection, using structured information extraction across research objects, states and data, models and tasks, execution interfaces, result feedback, and validation conditions. Digital models, digital shadows, state synchronization, decision support, and physical execution are distinguished by the relationships actually reported in the literature, rather than treated as levels of a fixed maturity ranking; missing descriptions are treated as limits of available evidence rather than as proof that a function does not exist. The technical discussion traces the pathway from sensing and digital representation to model construction, prediction, decision-making, and field execution, and compares process-based, agent-based, and data-driven modeling approaches together with their coupling mechanisms. Model accuracy under one dataset or environment does not establish validity under a different season, location, cultivar, or machine configuration. Four scenario groups—protected agriculture and plant factories, crop phenotyping and growth simulation, livestock and aquaculture, and agricultural machinery and robots—illustrate these differences. In protected agriculture and plant factories, centralized sensing and available environmental control equipment support microclimate monitoring, spatial representation, and increasingly prediction-based control, though reported evidence rarely extends to automatic execution across full production cycles. In crop phenotyping and growth simulation, structural reconstruction and process-based models support growth prediction and cross-location transfer, but such studies typically report model performance rather than field execution or management records. In livestock and aquaculture, digital twins mainly support behavior recognition, state prediction, and structural monitoring; evidence linking these outputs to feeding, environmental control, or other field operations remains limited, and individual differences and long production cycles complicate validation. In agricultural machinery and robotics, digital twins are used for virtual testing and simulation-to-real transfer, with some studies reporting field sowing and harvesting tasks; communication delays, safety, and recovery under continuous operation are less consistently reported than single-trial performance. Across all four groups, an operational loop requires evidence linking a model or decision output to a trigger, an executing actor, an actual action, and a returned result used in subsequent updates; an architecture diagram or control-related terminology alone does not establish this sequence. [Conclusions and Prospects] Existing strengths concentrate in sensing, digital representation, modeling, and prediction; evidence for sustained operation, execution feedback, maintenance, and cross-scenario reuse is reported less consistently. Future work should document object identification, calibration, and model versioning over time; trace execution permissions, commands, and feedback when outputs enter production; and record failures, interventions, and recalibration over extended validation periods. Transfer studies should specify which data, model components, and interfaces can be reused and which require reconfiguration, and comparisons require consistent definitions of tasks, events, and metrics. As this review is limited by its search scope, these priorities indicate directions for further verification rather than a complete account of deployed systems.

Key words: agricultural digital twins, technical system, digital agriculture, engineering operation, repeatable deployment, digital representation