# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 6 Conclusions  
In conclusion, our SLR emphasizes the trend and potential of DTs in the domain of agriculture water management. As the agricultural sector increasingly recognizes the importance of sustainable water use, DTs emerge as a promising tool, yet their adoption is still in nascent stages. The complexity and heterogeneity of agricultural environments present significant challenges to the widespread implementation of DT technology. The current applications of DTs in agriculture water primarily include irrigation, hydroponics, aquaponics, and vertical farming. These applications are predominantly in their initial phases, often confined to prototype stages. The majority of data acquisition for these DT systems is facilitated through AI, ML algorithms, IoT technologies, and remote sensing, which enable real-time data collection, monitoring, and prediction.  
The types of DTs, both with and without AI technologies, discussed in the reviewed studies primarily focus on monitoring and predictive capabilities. These enable the precise monitoring of water quality in hydroponics and aquaponics systems and the prediction of irrigation needs.  
Future Perspective  
Virtual models utilizing real-time and continuous data about agricultural assets can predict and resolve hidden problems in the field. DTs offer solutions to minimize weather-related risks by providing real-time forecasting and predictive simulations. By integrating data from satellites, weather stations, and IoT-enabled field sensors, DT models generate highly accurate weather predictions, allowing farmers to optimize planting, irrigation, and harvesting schedules. For instance, a study by [PERSON] and [PERSON] (2016) demonstrated the successful application of a DT system in an open-field mandarin orchard, where predictive analytics helped farmers mitigate weather-induced hazards, optimize resource allocation, and significantly reduce crop losses. Similar approaches have been employed in the context of agricultural machinery. A recent study conducted by [PERSON] et al. (2018) has demonstrated the effective development of a DT specifically designed for combine harvesters. This system integrates detailed modeling of component motions, real-time sensor data collection, and predictive analytics, significantly enhancing operational efficiency and accuracy in harvesting tasks. However, there is a noticeable absence of comparative studies between traditional CPSs and those integrated with AI and DTs, which could provide valuable insights into the relative advantages and drawbacks of these approaches. Future research needs to address critical aspects such as the quantifiable savings in water usage, its integration with climate models, the efficiency of the system, the economic feasibility of DT implementation, and its impact on agricultural yield. Works on these aspects will directly address the application of DT in agriculture water management. To realize its full potential, further research and development are necessary, focusing on practical applications, economic assessments, and collaborative efforts to bridge the gap between theoretical concepts and real-world implementation.  
This research received no external funding.