# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 4 Reviewing DT Applications in Agriculture and Farming Domain
### The Second Categorization of Researched Literature, Related to Specific Technical Research Aspects
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Citation** & **Case** & **Type of** & **DT** & **Target** & **Sensors** & **IoT/Platforms** & **Deployments/ Technologies-Protocols** \\ \hline
[81] & \(\surd\) & Journal Article & \(\surd\) & DT requirements for vertical farms & Temperature, humidity, and relative CO\({}_{2}\), Data Acquisition module & & Digital, PWM, I2C, SPI, Serial, network \\ \hline
[82] & \(\surd\) & Journal Article & \(\surd\) & DT for the greenhouse production process (WP4) & & & \\ \hline
[64] & \(\surd\) & Journal Article & \(\surd\) & A digital modelling approach to the food process & - & - & Automatically controlling system actuators \\ \hline
[27] & - & Review Paper & \(\surd\) & Data acquisition for automatically controlled actuator systems in agriculture & & & \\ \hline
[29] & - & Review Paper & - & A general framework of digital twins in soil, irrigation, robotics, farm machinery & - & - & Data recording, artificial \\ \hline
[9] & Descriptive & Review Paper & Model & Agriculture supply chain management & Bin level, flow, and identification sensors, RFID-DNA tags & & \\ \hline
[83] & - & Review Paper & - & Monitoring activities of livestock, optimization of crops, reduction of emissions to air, soil, and water & & \\ \hline
[63] & - & Review Paper & - & New farming methods supported by the DT & Monitoring physical entity crops state, resource optimization, and cultivation support & & \\ \hline
[63] & - & Review Paper & - & IoT Data-driven food production. & - & Climate control, energy, and lighting & Monitoring, optimization for controlling and autonomy \\ \hline
[85] & - & Review Paper & - & Virtualization of an agro-food supply chain & - & IoT concepts & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._DTs go along with monitoring systems to gather and analyze information. Sensors pose a critical role in smart agriculture by facilitating the collection of real-time data and offering valuable insights into a wide range of environmental and agricultural parameters. Field or soil probes that measure air and ground temperature, humidity, soil moisture, and ambient light [28; 45; 73; 81; 82; 88], added to CO\({}_{2}\) sensors measuring relative CO\({}_{2}\) concentration [80; 81], and infrared (IR) thermometers provide data acquisition modules for automatically controlled actuator systems in agriculture approaching digital modelling to the food process [64] monitoring activities of livestock, optimization of crops, reducing emissions to air, soil, and water. Drone image cameras [88] and high-resolution photographic cameras seem ideal for AR and VR by constructing virtual natural low-polygon 3D plant models as proposed in [35; 74]. Building Information Modeling (BIM) models of high-tech data-driven greenhouses are made feasible as in [79] and provide a VR presentation of the farm or field with the aid of a head-mounted display (HMD) with two handheld controllers serving as handy sensors for manual stimulus. Mini light detection and ranging