# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 1 Introduction  
Agriculture holds a pivotal role in global economies and societies and is fundamental to human survival and development. Agricultural practices influence environmental sustainability, impacting soil health, water resources, and biodiversity. Understanding and innovating in agricultural practices is crucial for addressing global challenges such as food security, rural development, and sustainable resource management. Key agricultural inputs\(-\)fertilizers, agrochemicals, and water\(-\)are critical for enhancing crop productivity [1]. The effective management of these resources is essentialto ensure agricultural sustainability and environmental protection. Water stands as the most utilized input among these resources [2], playing a vital role in crop irrigation. However, water scarcity is one of the most pressing challenges of our time, with significant implications for crop yield and food security [3]. Efficient irrigation systems and sustainable farming practices are crucial in mitigating the impact of water scarcity on agriculture. An improper management of irrigation development can lead to the dual challenges of rising aquifer levels and increased salinization in irrigated areas [4]. Various strategies can be explored to address this problem. But, due to the site-specific nature of these solutions, field experiments alone are insufficient to fully validate their effectiveness [5]. Precision irrigation systems now deliver water directly to crop roots, reducing water loss from evaporation and runoff [6; 7]. Additionally, emerging innovations such as microturbine technology have shown potential for enhancing energy efficiency and resource management in agricultural irrigation systems [8]. In recent decades, artificial intelligence (AI) has increasingly been adopted in the agricultural sector, significantly improving agronomic practices [9]. Whether it is in the field of genome-to-phenome prediction, soil health, farm machinery, or water management, AI is proving to be transformative [10; 11; 12]. Predicting phenotypic traits from genomic data remains a challenge, but AI combined with machine learning (ML) enhances accuracy and efficacy in this area, optimizing crop production [13]. In soil health, AI has been employed to predict nutrient availability and microbial respiration sensitivity, elucidating the role of the microbiome and environmental factors in the soil carbon cycle [14]. In farm machinery, AI is being used to design and automate operations for tractors, harvesters, and sprayers, improving efficiency and precision [12]. In water management, the precision prediction of water needs and irrigation practices is advancing, driven by AI-enabled tools and technologies [15]. In particular, the development of computer-based technologies such as simulations, digital models (DMs), remote sensing (RS) data, Geographic Information Systems (GISs), ML, deep learning (DL), and the Internet of Things (IoT) has greatly enhanced the understanding of parameters characterizing water resource management. Simultaneously, digitalization has become increasingly prominent, with the adoption of new technologies and concepts such as smart agriculture, precision farming, and, more recently, digital twins (DTs). In the agricultural sector, the adoption of DT technology is steadily increasing [16], but it is still less mature compared to fields like manufacturing [17]. This lag is due to the unique complexity of agriculture, which involves the interaction of climatic conditions, living organisms, and non-living physical elements [18]. Although artificial intelligence (AI) is not yet widely applied in agriculture, its potential is immense. The integration of AI with DTs could revolutionize the sector, thanks to AI's ability to analyze large volumes of data, identify complex patterns, and make accurate predictions. In particular, AI can improve water resource management, optimize irrigation cycles, predict adverse weather conditions, and monitor crop health in real time [9].