# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.1 Digital Twins in Irrigation  
[PERSON] et al. [93] designed a DT for managing irrigation in smart farming with a focus on water conservation. The system integrates a FIWARE-based IoT platform with a discrete event simulation model powered by Siemens Plant Simulation software. It collects and processes data related to soil, weather, and crops to generate daily irrigation recommendations. A Fuzzy Inference System, a form of rule-based AI, was employed to generate irrigation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **Application** & **AI Integration** & **DT Maturity** & **Technology Used** & **Reference** & **Author** \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Irrigation} & Artificial Neural & & & & \\  & & Networks & & & & \\  & & (ANNs), Random & & & & \\  & & Forest (RF), & & & & \\  & & Support Vector & & & & \\  & & Machine (SVM), & & & & \\  & & and Adaptive & & & & \\  & & Boosting used for & & & & \\  & & irrigation & & & & \\  & & management & & & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Irrigation} & Adaptive & & & & & \\  & & Neuro-Fuzzy & & & & \\  & & Inference System & & IoT, thermal imaging for & & \\  & & (ANFIS) used to & & & & \\  & & calculate & & & IoT platform & & \\  & & & requirement & & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{Plant Water} & Genetic & & & & & \\  & & Algorithms & & & & \\  & & (GAs) & & & & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{Plant Water} & No Information & Prototype & Satellite images, sensors (soil, & \\  & & & moisture) & & & \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{Vertical farming} & AI used but no mention of any specific technique & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{Aquaponics} & Linear regression & & & & \\  & & (I.K), Support & & & & \\  & & Vector & & & & \\  & Regression (SVR), & & & & & \\  & XGB, CART & & & & & \\  & & decision trees & & & & \\  & & models used to & & & (DDDAS) & \\  & & & & & \\  & & & & variables & & \\ \hline \multirow{4}{*}{9} & \multirow{4}{*}{Aquaponics} & No information & & & & \\  & & about ML & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{10} & \multirow{4}{*}{Hydroonics} & No information about ML & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{11} & \multirow{4}{*}{Hydroponics} & DL is used to estimate growth rate and weight of plants & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)recommendations based on environmental data. The DT enables a continuous real-time exchange of data between physical elements, such as sensors and actuators, and their digital models. This method aims to increase water use efficiency, optimize irrigation practices, and allow for the testing of various irrigation strategies before they are implemented in the field. [PERSON] et al. [94] developed a DT system for automated irrigation scheduling in a commercial vineyard utilizing Sentinel-2 biophysical variables. The study validated the use of near real-time fAPAR data to optimize irrigation through regulated deficit irrigation (RDI) strategies. This DT integrated soil moisture sensors, weather data, and remote sensing inputs to determine precise irrigation requirements. The findings indicated strong correlations between fAPAR and in situ measurements, enabling efficient water management. The system allowed for adaptive responses to changing conditions, ensuring optimal water usage and enhancing vine stress management during various growth stages. [PERSON] et al. [95] developed a DT framework for managing smart greenhouses, utilizing next-generation mobile networks and ML. This system employs the IBM Watson platform for IoT operations. They developed ML algorithms for predictive analytics and decision-making, enabling the activation of irrigation systems based on humidity levels and the regulation of internal greenhouse temperatures.