# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### DT Typology  
Then, we classified the types of DTs used in these cases. According to [PERSON] et al. [83], DTs can be categorized into six different types, though in practice, these features and classifications may overlap and are not always mutually exclusive. These types are described in more detail below. Imaginary: An imaginary DT represents an object or system that has not yet been physically created. It contains all the essential information required to materialize its physical version, including functional specifications and 3D models. This type is useful for conceptualizing new designs and ideas; imaginary DTs are purely hypothetical and do not provide the data analysis capabilities or maintenance benefits of DTs based on existing physical entities [84]. Monitoring: A monitoring DT is a digital counterpart that reflects the real-time or near real-time state and behavior of a physical item. It functions to monitor the performance and environmental factors surrounding its physical counterpart [85]. This form of DT delivers insights into what is happening or has previously happened with the connected object. It also provides diagnostic insights, explaining the reasons behind these events by connecting the object with relevant contextual data. While it excels in real-time issue identification and resolution, optimizing production and reducing downtime, it does not inherently offer predictive or prescriptive insights. Predictive: A predictive DT is a digital representation that forecasts the states and behaviors of physical things with predictive analytics, including statistical forecasting, simulations, and ML techniques. It operates dynamically, relying on real-time or near-real-time data from the physical twin. This type of DT is utilized in various industries to enable proactive maintenance and optimization [86], thereby reducing downtime and enhancing efficiency. However, it requires substantial historical data for accurate predictions and may not account for unexpected events or environmental changes [87]. Prescriptive: A prescriptive DT is an intelligent digital entity that recommends corrective and preventive actions for real-life objects using optimization algorithms and expert heuristics. It leverages the outputs from monitoring and predictive DTs to suggest actions that can achieve desired outcomes. Prescriptive DTs provide actionable insights to improve performance and enable proactive maintenance and optimization [88]. However, these recommendations require substantial historical data for accuracy and may not account for unforeseen events or environmental changes. Human decision-makers ultimately trigger and execute the recommended interventions [89]. Autonomous: An autonomous DT operates independently, fully controlling the behavior of real-life objects without human intervention. These twins can self-learn, self-diagnose, and adapt to user preferences, optimizing performance and reducing downtime. While they reduce the need for human involvement and enhance efficiency, they require substantial historical data and complex algorithms for accurate decision-making and may not account for unforeseen events or changes in the environment [90; 91]. Recollection: A recollection DT maintains a comprehensive historical record of physical objects that no longer exist in real life, serving as a digital memory [92]. This type of DT captures and stores data about a physical entity for future analysis, which can help reduce environmental impacts of disposal, optimize future generations of objects, and trace product quality and safety issues. While it is valuable for analyzing past performance and optimizingnew designs, it is limited to historical data and cannot provide real-time insights, requiring sensors and other technologies for data capture. Due to the overlapping functions of these categories, drawing clear distinctions between them proves challenging. Despite this, we tried to apply these categories to our studies to explore their practical applications in agricultural water management.