# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 5 Discussion  
The reviewed literature highlights the growing adoption of DT technology in agriculture, particularly in water management. DTs have demonstrated their potential to address critical challenges related to water scarcity, sustainability, and efficiency. In irrigation, DTs are employed to monitor soil moisture, evapotranspiration, and predict irrigation water requirements by integrating ML, models, AI, IoT sensor data, remote sensing data, and decision support models [108]. These technologies also facilitate the management of plant water availability, which impacts growth and productivity through inputs such as rainfall, irrigation, and soil moisture [109]. DT applications extend beyond traditional irrigation systems to advanced setups such as hydroponics and aquaponics, where they maintain water quality by continuously monitoring parameters like pH, nutrient concentration, and temperature. This ensures the health and productivity of both plants and fish, preventing potential issues through real-time adjustments. The analysis indicates that DTs are primarily used for predictive purposes, focusing on soil-water interactions and water quality management. Based on the review, DT applications can be categorized into monitoring DTs and predictive DTs. Monitoring DTs emphasize real-time data collection and immediate response to changes in water quality and environmental conditions, crucial for systems like aquaponics, hydroponics, and vertical farming. Predictive DTs, on the other hand, forecast future water requirements by integrating weather data and other environmental variables, enabling proactive irrigation management to optimize water use and improve crop yields. Several tools and platforms were identified as integral to DT systems. MongoDB, a NoSQL database, is particularly effective in handling large volumes of unstructured data generated by IoT devices and remote sensing systems, ensuring scalability and efficiency in data storage [110]. Node-RED, a flow-based development tool, integrates IoT devices, streamlining the collection and processing of sensor data for real-time decision-making in DT systems [111]. R Dashboards enable real-time visualization of system performance, providing actionable insights to enhance water management and system efficiency [112]. Python, due to its extensive libraries such as Pandas, NumPy, and Scikit-learn, supports AI development by enabling data analysis, model building, and automation tasks [113]. TensorFlow and Keras simplify the creation and training of deep learning models, allowing for advanced predictive analytics [114], such as forecasting irrigation needs or simulating crop growth scenarios under different environmental conditions. Raspberry Pi, Ubidots, and FIWARE aggregate sensor data, feeding them into DT systems for real-time monitoring and predictive analytics. Despite their potential, most studies remain at the prototype stage, with only a few implemented in actual field conditions. This highlights significant challenges, including the complexity and variability of agricultural tural environments, the need for reliable high-resolution data, and the integration of diverse sensor types. DT technologies also provide substantial benefits across various agricultural practices. These include real-time monitoring and control of water usage, predictive maintenance to minimize downtime, resource optimization in water-scarce areas, and enhanced decision-making based on comprehensive data. Additionally, DTs contribute to cost savings through optimized water use and reduced maintenance expenses while promoting sustainability by minimizing water waste and environmental impact. Their versatility allows customization and scalability across different agricultural setups. Moreover, DT technology has been applied in several agriculture-related areas, including crop production [115], livestock management [116], and farm robotics [117]. In crop production, DTs support monitoring, resource optimization and decision-making for cultivation practices.