## 1 Introduction  
The challenge of feeding a growing population with finite resources is becoming increasingly pressing. By 2050, the world population is expected to reach 9.7 billion, necessitating a 70% increase in food production [1]. Irrigation plays a crucial role in enhancing crop yields and agricultural productivity to meet this growing demand. Studies have shown that irrigation can significantly increase crop water productivity, contributing to increased food production [2, 3]. However, water scarcity poses a significant challenge, with many regions facing water deficits and the need for improved water management practices [4]. Optimizing irrigation schedules and doses based on crop water requirements and environmental conditions like evaporative demand is essential for maximizing the yield and quality while minimizing water use [5]. The necessity of scalable water-efficientpractices for increasing food demand cannot be overstated. Techniques such as regulated deficit irrigation, the use of treated wastewater for irrigation, and the cultivation of drought-tolerant crops like sorghum and cotton have shown promise in improving water productivity and ensuring food security [6; 7]. As the global food challenge intensifies, it is imperative to critically evaluate the current state and future potential of irrigation management systems to guide the research, innovation, and implementation efforts towards fully autonomous and scalable solutions. Despite the importance of irrigation in addressing the global food challenge, traditional irrigation management techniques have significant limitations. These techniques include manual scheduling, which relies on crop observation, soil feel, and intuition; timer-based scheduling, where irrigation occurs at predetermined intervals regardless of actual crop needs; and sensor-based methods using ET modeling and soil moisture sensing. Manual and timer-based scheduling can lead to high operational costs and inefficient water use [8]. The reliance on manual intervention and predetermined schedules limits their adaptability to changing environmental conditions, crop water requirements, and soil moisture levels [9]. Sensor-based irrigation systems offer an alternative, enabling real-time adjustments based on soil water status measurements [9]. However, the adoption of these systems in commercial settings has been limited, often requiring extensive input from researchers [10]. The limitations of traditional irrigation management techniques highlight the need for scalable, automated solutions for greater efficiency in irrigation management. Automated systems that collect real-time data, analyze it, and make autonomous irrigation decisions can lead to improved water use efficiency, increased crop productivity, and significant energy savings [8; 11]. The recent studies have demonstrated that smart irrigation systems utilizing IoT and machine learning technologies can reduce energy consumption by 30-40% compared to traditional methods by optimizing pump operation and water delivery schedules [12; 13]. These energy savings are particularly significant given rising energy costs in agricultural operations, making automated systems increasingly attractive for both environmental and economic sustainability [14]. The optimization of irrigation scheduling and water delivery not only conserves water resources but also minimizes the energy required for pumping and distribution, leading to reduced operational costs while maintaining or improving crop yields. The emergence of smart irrigation management and the Internet of Things (IoT) marks a significant shift from historical irrigation practices. Smart irrigation management involves using advanced technologies and data-driven approaches to optimize water usage and improve crop yield. IoT, which refers to the interconnection of devices and sensors through the internet, enables the collection and exchange of vast amounts of data in real-time.