## 1 Introduction  
Food insecurity is a pervasive and pressing global issue that has far-reaching consequences for individuals, communities, and nations. Given that the world population in 2022 reached almost 8 billion and is expected to exceed 10 billion by the later part of the 21 st century [1], the Food and Agriculture Organization (FAO) of the United Nations predicts that the production of food commodities needs to increase by at least 50% compared to 2012 [2]. On top of that, food insecurity is exacerbated by the intensification of occurrences of unusually severe weather events due to climate change and the economic crises, as well as ongoing armed conflicts like the Russo-Ukrainian war or other contingent circumstances, for instance, the COVID-19 pandemic [3]. Therefore, it seems imperative to ensure food security through higher agricultural productivity without compromising the long-term sustainability and resilience of agro-ecosystems [4]. In this sense, agricultural production systems face the challenge of meeting a rising food demand along with climate change-related conditions, the deterioration of natural resources due to their uncontrolled exploitation, the loss of biodiversity, and the increased food waste caused by the deficiencies in the agri-food supply chain while remaining profitable [5]. Considering the importance of the agricultural production system as a main pillar of the primary sector of industry, various scientific and technological approaches have been devised and implemented over time to increase the efficiency of their outputs [6]. Indeed, the agricultural sector has evolved throughout history, alongside the technological developments of all industrial revolutions, as illustrated in Figure 1, from the first agricultural revolution under the term \"Agriculture 1.0\" to the current agricultural revolution, attributed as \"Agriculture 5.0\" [5; 7], which represents the emerging trend that the agricultural sector is going to adopt in the upcoming years. In particular, the concept of Agriculture 5.0 focuses on transforming agricultural production systems through the integration of the recent advancements in smart technologies [8] so as to enhance their efficiency while making them environmentally sustainable [9]. Rapid progress in the application of leading-edge technologies, such as wireless sensor networks (WSNs) and real-time sensing [10], artificial intelligence (AI) [11] and machine learning (ML) [12], edge computing [13], big data analytics and the Internet of Things (IoT) [14] as part of Agriculture 4.0 [15], are enabling agricultural production systems to be fully data-driven in the context of Agriculture 5.0 [16] and, therefore, raise attention in the digital twins (DTs) concept and their feasible applications in the agricultural sector to achieve the objectives of sustainable development [17]. Actually, based on the integrated farm management (IFM) approach [18], an agricultural production system is potent at adapting to dynamic changes [19] as it is updated on a continuous basis and in real time based on data feedback from sensors and devices that collect the parameters related to the properties of its physical entities and environment, as well as the system itself [8; 20]. In this respect, the DTs concept enables the virtual replication of physical entities with corresponding digital ones that are accurate and \"live\" [21], bridging them according to  
Figure 1: Timeline of industrial and agricultural revolutions. a cyber-physical architecture under which any change that occurs in the physical entities simultaneously and continuously modifies their virtual twins [22]. The potential of applying the DTs concept and the principles it represents to all industrial sectors, including the agricultural one, has lately gained great research interest, as reported in the recent literature [22; 23; 24; 25; 26; 27], concerning the case of arable agriculture in particular. In this area, DTs represent virtual representations of extensive fields.