# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 5 Discussion  
A first attempt to comment on specific aspects of the reviewed literature has been done in Section 2. In total, 14 journal articles or conference papers, including a published book, propose case studies that support the idea of DT implementation in agriculture, contrary to the rest published review papers that suffice to merely describe the topic and record the achievements of the scientific community referring to it. It is interesting to point out that three (3) relevant case study conference papers and a published book, added to nine (9) journal articles, seem to slightly overcome the amount of the 12 review papers in a total of 26 articles. The limited practical application of digital twin technologies is obvious, even stated in the literature by the authors. Even though [9; 86; 87] are review papers, the authors either describe the necessity of a DT model as in [9] for the agriculture supply chain management domain, even addressing sensors such as level, flow, identification sensors, RFID or DNA tags, or highlight the need for future study on DT models as in [86] making use of remote detection and monitoring schemes, LiDAR sensors, passive microwave sensing devices, RADARs or even sensors onboard UAVs and satellites, for monitoring, controlling, coordinating, and executing farm operations at agricultural sites by IoT sensor nodes as in [87]. The significance of using Sensors as part of a dynamic IoT control-oriented mechanism as demanded in DT infrastructure should be acknowledged as in [35; 73; 74; 77; 79; 81; 87; 88] where soil probes, drone image cameras, multispectral and photographic cameras, IR thermometers, mini-LiDAR sensors, a mesh of sensors measuring temperature, humidity,luminosity, and relative CO\({}_{2}\) concentration, and air velocity are addressed as essential elements to materialize relevant IoT platforms accordingly. In [28; 73; 75; 88], the SWAMP IoT-based smart water management platform is mentioned. In [19; 26], the IoT scheme is merely addressed, and in the journal [82], IoT is only mentioned as well in the review papers [63; 85; 87], where the IoT is stated as a promising concept. The fact that in [73] an OPC UA server is recommended for implementation, in [77] an Arduino single-board computer is used as well as the Raspberry Pi-3 platform as in [80; 81] in a total of 14 articles that either describe the use of IoT platforms or vaguely present a case study project [9; 19; 26; 28; 35; 64; 74; 75; 76; 77; 78; 79; 80; 81; 82] means that the practical implementation of IoT platforms is quite limited for the time being. The necessary software to operate such platforms is focused on Python [28; 77], dedicated FIS, Json, FIWARE Cygnus connector, IoT Agent, OPC agent [73], RPi in [77; 80], Arduino IDE n [77] relevant to the case studies previously mentioned. These make use of Ethernet communication technologies [28; 73; 77], LoRa [73], and Wi-Fi, [77] in addition to [35; 74] that make use of Wi-Fi and mobile LTE [74] communication technologies that facilitate near real-time interaction with the environment. The concept and definition of DT seem to be continuously evolving, but in general, it refers to a specific collection of digitally or virtually materialized models that represent a physical process or object. These models are used for simulation and future behavior forecasting, guiding the feedback loop from the Actuators and Control node to the Physical entity, as profoundly visualized in Figures 3-5. The ongoing feedback loop between the physical farm and the DT is imperative to update the DT model to optimize and enhance performance and efficiency. Several attempts have been made in this direction. In the reviews [9; 29; 63; 83; 86], the concept of the DT model is proposed merely as an evolving concept without an implemented case study.