# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 2 Background
### DT Architecture  
A comprehensive and foundational framework for the first DT was established in 2014, marking a significant milestone in the field. This framework encapsulates not only the physical realm but also extends to the virtual realm, highlighting the intricate connection that acts as a crucial link between them [39]. While effective for basic monitoring and visualization, this model lacks the depth to support advanced capabilities such as intelligent control, predictive maintenance and closed-loop feedback. [PERSON] et al. [40] described DTs as a unique digital representation of a physical asset, composed of three key elements. The Digital Master refers to the static, model-based design of the asset. The Digital Shadow captures real-time operational and condition data collected from the physical system. These two components are connected through an intelligent linkage, which includes algorithms, simulations, and decision-making processes. This structured separation enables more advanced behavior modeling, supports simulation-based optimization, and allows for continuous system learning. As a result, the three-layer DT architecture is well suited for the evolving demands of Cyber-Physical Production Systems (CPPS). This three-layer DT lacked explicit mechanisms for managingservices and handling large-scale, heterogeneous data for Prognostics and Health Management (PHM). To address this gap, [PERSON] et al. [41] presented an extended five-layer DT model, which has two extended layers, a Service Layer to model and manage functional operations like monitoring, calibration, and fault diagnosis, and a Data Layer to integrate and fuse data from physical systems, simulations, services, and domain knowledge. So, this DT has the ability to handle data from multiple sensors. Further contributions to the development of DT architectures include the five-layer DT model by [PERSON] et al. [42]. This DT is quite different from the previous extended five-layer DT. While [PERSON] et al. 's architecture was mainly designed for managing PHM, [PERSON]'s model takes a more system-level, IT-focused approach that spans from CPS to distributed computing and user interfaces. It introduces practical elements such as APIs, cloud-based storage systems, and visualization platforms. This kind of structure is more suitable for industrial environments where systems are complex and need to be monitored, controlled, and updated in real time. These DTs focused on system modeling, data storage, and user interaction. However, they lacked sufficient granularity to separate edge-device logic, communication handling, and simulation intelligence. Further, [PERSON] et al. [43] introduced an extended six-layer DT structure by isolating control logic, local data storage, IoT gateways, cloud databases, and simulation components. The introduction of a dedicated IoT gateway layer plays a pivotal role, enabling data filtering, conflict resolution, safety validation, and efficient transformation between local and cloud data. This enhancement addresses the limitations of earlier models by facilitating bidirectional, secure, and scalable communication between physical and cyber spaces. This DT architecture is more suitable for applications involving multiple interconnected DTs. The differences between the DT architectures defined above are described in Figure 1.  
The DT architecture can be conceptualized as a structure with three main layers [44]. The first one is a physical layer: the physical layer encompasses real-world entities, categorized according to their stage in the product life cycle. The second is a network layer, which acts as a bridge between the physical and digital domains, enabling data exchange  
Figure 1: Layer-wise evolution of DT architectures from simplified to extended models synthesized from the literature. and communication. The third is a computing layer, which includes digital representations of real entities, utilizing data-driven and physics-based models, along with analytics, services, and user interfaces.