## 2 Background and Related Work  
Digital twin technology has emerged as a powerful paradigm for modeling and managing complex systems by maintaining a synchronized representation between physical entities and their virtual counterparts in real time. Originally conceptualized by [PERSON] in the early 2000s [7; 8], the roots of digital twins trace back to NASA's use of mirrored simulation systems for spacecraft monitoring in the 1970s [9]. NASA later defined a digital twin as \"an integrated multi-physics, multi-scale, probabilistic simulation of a system that uses the best available models, sensor data, and fleet history to mirror its physical counterpart\" [10]. This foundational concept has since been adopted across sectors such as aerospace, automotive, manufacturing, and energy [11; 12; 13]. In the domain of Product Lifecycle Management, digital twins have demonstrated value in enabling continuous, data-informed decision-making throughout the life cycle of engineered assets. These implementations typically rely on structured environments with well-characterized sensor integration, standardized control systems, and repeatable operating conditions [12], particularly within industrial and engineering contexts. By contrast, the application of digital twins in agriculture presents distinctive challenges. Agricultural environments are unstructured, biologically heterogeneous, and subject to temporal and climatic variability. Furthermore, they involve complex interactions with living systems--including plants, insects, and microorganisms--that require perceptual adaptability, semantic reasoning, and robust real-time control strategies [14; 15]. These characteristics complicate the development of accurate and responsive virtual representations. Several recent reviews emphasize this complexity, highlighting the need for ontological frameworks, AIoT-driven architectures, and integration strategies capable of supporting interoperability and reasoning [16; 17; 18]. Initial explorations of digital twins in agriculture have primarily focused on isolated functions, such as field monitoring, crop yield estimation, and irrigation control. For example, [PERSON] et al. proposed a conceptual classification of agricultural digital twins based on the IoT-mediated integration of field data with simulation models [14]. [PERSON] et al. emphasized the need to transition from conceptual prototypes to operational frameworks that tightly couple sensing, modeling, decision-making, and actuation [15]. More recently, comprehensive analyses by [PERSON] and [PERSON], as well as [PERSON] et al., have documented that most digital twin implementations in agriculture remain at early stages of development and that current approaches are often fragmented and lacking integration [19; 20]. Parallel advances in agricultural robotics have led to the development of mobile systems capable of performing targeted tasks such as fruit detection, harvesting, and environmental monitoring. Notable examples include cucumber-picking robots [4] and dual-arm platforms for automated aubergine harvesting [6]. However, these systems are typically deployed in isolation, without integration into a digital twin feedback loop. Consequently, they lack predictive modeling, real-time simulation, and semantic feedback, core components of the digital twin concept. To date, few research studies have attempted to bridge robotic automation with digital twin architectures in agriculture. Existing approaches often rely on simplified robotic models and remain limited to passive data acquisition or remote supervision. Architectures capable of active learning, semantic environment modeling, or hybrid edge-cloud control remain rare. Furthermore, current systems seldom exploit feedback mechanisms that combine task planning, execution, and policy refinement in closed-loop form. Recent contributions, such as those by [PERSON] et al.