## 5 Discussion  
The DigiHortiRobot architecture demonstrates a structured and scalable integration of digital twin technology with real robotic systems in greenhouse horticulture. Its three-phase implementation strategy enables a progressive transition from environmental monitoring to autonomous task execution, ensuring technological readiness and deployment feasibility. A core contribution of the system lies in its closed-loop execution cycle, which links perception, simulation, decision-making, and actuation into a continuous and adaptive pipeline. In contrast to many existing digital twin approaches in agriculture, typically limited to passive data monitoring, visualization, or off-line simulations, DigiHortiRobot enables bidirectional synchronization with a physical robotic agent, capable of real-time intervention. For example, while [PERSON] and [PERSON] (2023) provided a detailed review of agricultural digital twins, they emphasized that most implementations remain conceptual and rarely progress beyond simulation or dashboard-level integration [19]. Similarly, [PERSON] et al. (2025) highlighted that current solutions are often fragmented, focusing on specific components such as sensing or visualization without closing the perception-action loop [20]. DigiHorticRobot addresses this gap by embedding semantic reasoning and predictive simulation into a platform that supports robotic execution under real-world constraints. Technically, the inclusion of a dual-arm robotic platform distinguishes our system from prior work. For instance, [PERSON] et al. (2024) proposed a progressive transition from environmental monitoring to autonomous task execution, ensuring technological framework for greenhouse management that integrates machine learning and IoT communication layers, but does not interface with any real robotic hardware [21]. [PERSON] et al. (2024) explored automated software testing for agricultural digital twins, but their prototype remains virtual and lacks a physical execution layer [26]. By contrast, our platform not only maintains a synchronized digital representation of the greenhouse but also enables the physical execution of complex tasks such as seeding and harvesting. The use of 3D perception, semantic mapping, and goal-driven planning further allows the system to operate under dynamic spatial and phenotypic variability. The contribution of DigiHorticRobot also resides in its decision-making cycle. Unlike systems focused solely on visual feedback or statistical modeling, our approach uses the simulation-based evaluation of action policies that are generated in response to user-defined agronomic targets. This mechanism improves operational robustness by validating decisions in a risk-free virtual environment before physical execution. Although similar ideas have been proposed in the industrial digital twin literature, their adaptation to agriculture, where biological uncertainty and unstructured environments prevail, remains rare. Our implementation validates this approach using IoT-based perception, real-time mapping, and policy refinement grounded in actual feedback. Nonetheless, several limitations affect the system's current applicability and must be acknowledged. First, the generalization of the perception and planning models remains limited due to the morphological diversity across crop types. Although DigiHorticRobot has been validated with tomato and aubergine plants, their distinct structural characteristics, such as fruit arrangement, stem rigidity, and leaf density, mean that deployment to other crops would require additional perception tuning, the retraining of detection models, and the possible adaptation of manipulation strategies. This poses a challenge for short-term scalability in heterogeneous greenhouse settings. Second, the use of edge-cloud communication introduces latency, especially in phases involving large-scale image processing or task rescheduling.