# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 3 Related Work  
Recent reviews show that interest in DTs in agriculture has notably increased in the last few years. For instance, the authors of [27] presented an extensive literature survey on DTs in agriculture, establishing a foundational understanding of the topic. The authors of [36] examined the application of the DT paradigm in assessing soil quality, highlighting the technology's potential to revolutionise traditional practices. Further, [PERSON]. [13] introduced a pioneering case study on urban farming technology. This study explored how a cyber-physical aquaporics system, augmented with a DT and machine learning, could gain adaptive capabilities, showcasing the practical implementation and benefits of DTs in enhancing agricultural systems. [PERSON] et al. [11] analysed how DTs could advance smart farming. The authors defined the concept of a DT, developed a typology of different types of DTs, and proposed a conceptual framework for their design and implementation. The authors mentioned that DTs in agriculture include six distinct types: imaginary, monitoring, predictive, autonomous, and recollection DTs, each serving different control purposes. The authors also proposed a framework and applied and validated it in five smart farming use cases in the European IoF2020 project, covering arable farming, dairy farming, greenhouse horticulture, organic vegetable farming, and livestock farming. The case studies have offered valuable insights into how DTs can improve and optimise smart farming systems. In 2021, [PERSON] et al. [27] published a literature review on digital twins in agriculture from 2017 to 2020. The authors identified 28 use cases and compared them with other domains' use cases. The authors compared reported benefits, service categories, and technology readiness levels to assess the level of digital twin adoption in agriculture. The authors also proposed a roadmap inspired by other domains of DT applications. This paper concluded by identifying distinctive characteristics of agricultural DTs. However, this paper did not provide specific examples of implemented DT applications in agricultureor the latest advancements in this field. It also did not cover potential benefits in detail or the latest advancements in DT applications in agriculture. [PERSON] et al. [5] presented a general framework of DTs in soil irrigation, robotics, farm machinery, and food post-harvest processing in the agricultural field. The authors emphasised the need to develop DT systems further in the agricultural context. These systems should be able to monitor, record, and analyse data to predict and prescribe the best decision for digital farming management. Also, the authors identified the need for further research and development in applying DT concepts in agriculture, particularly in addressing practical challenges, integrating advanced technologies, and developing robust DT models for different aspects of agricultural operations. Few other studies are available that apply the concepts of DTs to smart agriculture. For example, Ref. [37] proposed a DTs-based smart agriculture framework utilising LoRaWAN for sensor networks in farm fields and the intelligent processing of aerial imagery to detect plant diseases and nutrient deficiencies. The authors of [13] have proposed a framework for a decision support system to coordinate decentralised urban agricultural production units. The authors of [38] discussed the principles of developing a multi-agent DT of plants using broccoli as an example. The results of the reviews clearly show that the concept of DTs in agriculture is still in its early stages and has not been fully explored in research. Although the agricultural sector presents numerous opportunities for the application of DT technology, its development and implementation are still in progress. Therefore, this paper proposes a set of DT use cases designed for the agricultural domain. The aim is to bridge the current research gap and contribute to sustainable agriculture and enhanced production goals.