# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 6 Discussion and Future Work  
In conclusion, the DT concept is a powerful innovation with promising applications in agriculture, particularly when integrated with other cutting-edge technologies such as multi-agent systems (MAS), cloud-fog-edge computing, and microservices. The rapid advancement in sensing, communication, and computing technologies has sped up the development of DT models. These models replicate real-world conditions with real-time data, improving farm operations and allowing for better planning by offering suggestions for different 'what-if' scenarios. This paper introduces a DT architecture and explains its components. While this architecture holds potential for numerous applications in agriculture, our current focus is on practical implementations, such as developing irrigation systems for crops, early disease detection, nutrient management recommendations, and identifying nutrient deficiency risks. The proposed structure uses agents and microservices to create and develop a cloud-fog-edge infrastructure, establishing the foundation for promising research in sustainable smart crop farming. The possible expected challenge would be accessing all the required data in the field. This data can be collected from sensors and third-party APIs for weather and other data. Implementing DT in agriculture demands solid technological infrastructure, including sensors, IoT devices, and cloud-fog-edge infrastructure. In addition, to apply this in the real world, the initial cost to install the required devices and technologies must be higher if it is not a smart farm. However, the proposed architecture is most suitable for smart agriculture. Thus, most smart farms already use smart agricultural devices such as drones,sprayers, sensors, etc. Regarding this architecture, cloud hosting and accessing accurate data from third-party APIs such as satellite data may also be expensive. Agriculture is generally a highly complex domain influenced by various unpredictable variables such as weather conditions and patterns, soil conditions, and other environmental factors. Although we get these predicted data from sensors and weather stations, it might still be unreliable. Nowadays, most farms are already using some level of technology and devices. Therefore, integrating with the existing systems in DT may be challenging. Implementing DTs involves collecting and analysing vast amounts of data, raising concerns about data privacy, ownership, and security. In addition to this, well-written documents and manuals should be prepared for use by agricultural stakeholders. From our perspective, the proposed architecture can be validated in several ways. For instance, it can be simulated with historical data, applied to a small-scale farm, and seen in the field without applying a digital twin. Additionally, it is possible to do qualitative testing based on farmers' feedback based on the user experience. Another possible way would be to check how the DT reacts to the real-time data. However, further research is needed to analyse the real challenges and benefits of the proposed architecture. We are in the process of implementing the proposed use cases. For our future work, we plan to concentrate on several areas, investigating the design constraints involved in implementing DTs with cloud-fog-edge computing and identifying the challenges and potential solutions in the domain of agricultural DTs. In addition to this, we are also planning to propose a framework to implement a DT for agriculture domain. The future work will be also focusing on how these DT can be evaluated in the real world. Conceptualization, Y.K., R.W. and R.C. ; Methodology, Y.K. and L.V. ; Writing--original draft, Y.K. ; Writing--review and editing, Y.K., L.V., R.W. and R.C. Supervision, R.C. All authors have read and agreed to the published version of the manuscript. CONSUS is funded under the SFI Strategic Partnership Programme (16/SPP/3296) and is co-funded by Origin Enterprises plc. (Dublin, Ireland). No new data were created or analyzed in this study.