# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
## 1 Introduction
According to [PERSON] et al. [17] and [PERSON] et al. [18], DT \"exploits sensed data, mathematical models and real-time data elaboration to forecast and optimise the behaviour of the physical asset at each life cycle phase, in real-time\". DTs have been adopted in different domains such as manufacturing, aviation, hospital management and precision medicine and safety amongst others; see [19; 20; 21]. DTs are digital models enhanced with bilateral communication between the physical and the cyber space [17]. In traditional simulation, the digital representation of an existing physical asset does not use any form of automated data exchange between the physical asset and the digital one. In a DT, the data flow between an existing physical asset and a digital one is fully integrated in both directions. In this way, the digital model is synchronized with the status of the physical asset and the results of the simulation can be directly implemented to optimize the physical asset. In the context of CEA, the DT ability to integrate the real-time status of the physical asset into simulation may be adopted to guide the decision-making in crop management and microclimate control for the optimization of productivity. To reach the aforementioned capabilities, the physical asset must be enhanced with a DT architecture consisting of (Figure 1):  
* Physical Asset: Target system to optimize through the DT architecture. * Digital Twin: Virtual test bed synchronized with the status of the physical asset that is responsible to evaluate the different 'what-if' scenarios that may optimize the system. * Intelligence Layer: Hosts the rules and the knowledge to choose among the alternatives tested in the DT. In the actual agricultural innovation systems, information-based management characterizes a technological phase called Farming 4.0 [23]. Few recent literature reviews can be found concerning the application of DT in agriculture. [PERSON] et al. [24] investigate the added-value of DTs for agriculture through the identification of 28 use cases, and their comparison with use cases from other disciplines. Based on their analysis, they examine the extent of the DT adoption in agriculture, shed light on the concept and the benefits it brings, and provide an application-based roadmap for a more extended adoption. [PERSON] et al. [25] first review the concept of DT in agriculture by showing how DTs can advance farming practices and by developing a typology of different types of DTs. Then, they propose a conceptual framework for designing and implementing DTs. [PERSON] and [PERSON] [26] analyze ways in which DTs can contribute to most of the life cycle phases of hydroponic systems such as designing, operation, monitoring, optimization, and maintenance, amongst others. In the aforementioned literature reviews, one of the potentialities of the DT for CEA applications is defined as the optimization of the productivity through the simulation and prediction of crop microclimate and growth. In their work, [PERSON] et al. [27] review the basics of microclimate models for greenhouses and the results obtained from their application. Furthermore, a review of crop growth models and functional-structural plant models is also provided. [PERSON] et al. [28] present the first advancements in the development of a DT that is intended to optimize the energy efficiency of greenhouses. [PERSON] et al. [29] develop an IoT-enabled structure for vertical farming that has the objective of enabling sustainable CEA systems. [PERSON] et al. [30] introduce a multifunctional environment, equipped with sensors and monitoring systems that allows the acquisition of data and their processing using mathematical yield models to optimize crop management. To the best of the authors' knowledge, the presented works constitute the most relevant ones in the productivity optimization through DTs. However, it can be noticed that a DT architecture for CEA systems potentially able to optimize productivity due to the utilized simulation software is not available yet.