# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
## 5 Conclusions and Future Work  
To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing the utilized resources. Controlled environment agriculture and digital twins may represent fundamental tools to reach the optimization of productivity, thus contributing to the planet's food security.  
With this in mind, the objective of this research work was the development of a DT architecture potentially able to optimize productivity in the context of CEA applications. The objective was reached by designing an architecture that utilizes (as DTs) simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to the crop management. The architecture was applied to a prototype greenhouse for its validation. Finally, communication latency was assessed as a means to test the achievement of the communications defined within the DT architecture.  
This work contributes to the research on DT in CEA systems by proposing an architecture potentially able to optimize productivity. The methodological approach and the identified tools can be utilized by companies for retrofitting their CEA systems with the  
Figure 7: Timing diagram of the latency taken within each communication. Error bars quantify the uncertainty of each measurement. The nomenclature shown in Figure 6 is utilized for defining the different communications.  
DT functionality, and from universities for the generation of automation and agricultural hands-on laboratories, and for the investigation of approaches for the optimization of the productivity in CEA systems.  
However, several works are still missing before the ability of the proposed architecture to optimize the productivity can be certified. In line with this goal, some future works are identified:  
* Optimization workflow: The optimization workflow identified within this work involves a sequence of two simulation software. Starting from the domain knowledge, optimization algorithms and/or heuristics must be studied to identify optimal solutions in acceptable computation time.
* Case study: After the definition of heuristics, a case study must be implemented and productivity must be optimized to certify the ability of the proposed DT architecture to optimize productivity.
* Architecture improvement: Some improvements should be investigated as the movement of the intelligence layer and the DTs to the cloud, and the simplification of the physical domain with the implementation of smart sensors and actuators that would make the controller and gateway unnecessary.  
Conceptualization, J.D.C. and D.S.-L.; methodology, J.D.C. and G.B.; software, J.D.C.; validation, J.D.C. and D.S.-L.; formal analysis, J.D.C. and D.S.-L.; investigation, J.D.C., D.S.-L. and G.B.; resources, G.B.; data curation, J.D.C. and D.S.-L.; writing--original draft preparation, J.D.C., D.S.-L. and G.B.; writing--review and editing, G.B.; visualization, J.D.C. and D.S.-L.; supervision, G.B.; project administration, G.B.; funding acquisition, G.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.