## 1 Introduction
Modern approaches rely on vast data and analysis algorithms, leveraging technologies such as remote sensing, sensor networks, weather data, and computational algorithms [15]. The IoT plays a vital role in collecting vast amounts of data through sensors, data transmission, and tailored networks, enabling real-time monitoring and control of irrigation systems [16]. These advancements in data collection and analysis have the potential to revolutionize irrigation management, allowing for more precise and efficient water use. However, challenges such as processing diverse data sources, data integration, and the lack of integrated data analysis hamper the full benefits of IoT in irrigation management [17]. The current fragmented approach in smart irrigation, focusing on individual components rather than the entire system, limits the potential for fully autonomous, real-time end-to-end irrigation management [15]. To address these challenges and fully realize the potential of smart irrigation management, there is a need for automating and integrating each section of the irrigation management pipeline, from sensor/weather data collection and transmission to processing, analysis, decision-making, and automated action. This integration requires a thorough investigation of the role of interoperability and standardization in enabling communication and compatibility between components within the automated irrigation management pipeline. On the other hand, machine learning (ML) plays a significant role in processing vast amounts of data, predicting plant water stress, modeling climate effects, and optimizing irrigation in smart irrigation management systems. ML algorithms, such as artificial neural networks and support vector machines, can analyze data collected from sensors and weather stations to determine optimal irrigation schedules based on patterns and insights gleaned from the data [18]. For example, ML models can predict crop water requirements based on historical weather data, soil moisture levels, and plant growth stages, enabling more precise and timely irrigation decisions. However, the potential of ML is often constrained by manual steps, such as data interpretation, decision making on irrigation timing and volume, and system adjustments. Automating ML integration to allow direct action from insights to irrigation execution, removing bottlenecks, and achieving real-time adaptability is crucial for fully autonomous irrigation management. By integrating ML into automated systems, the irrigation management pipeline can become more efficient, enabling real-time decision-making and action based on data-driven insights. To achieve this level of automation and integration, it is essential to identify gaps and propose solutions for integration across the automated irrigation management system, aiming to achieve fully autonomous and scalable irrigation management. To achieve integration across the automated irrigation management system, interoperability and standardization are critical. Interoperability allows different system components, such as sensors, actuators, and software, to communicate and exchange data effectively, while standardization ensures that data are represented in a consistent format [19; 20; 21]. The existing and emerging standards such as the Open Geospatial Consortium (OGC) SensorThings API [22] and ISO 11783 (tractors and machinery for agriculture and forestry--serial control and communications data network) [23] have applicability to real-time irrigation management systems. The OGC SensorThings API provides a standardized way of connecting IoT devices, managing and retrieving sensor data, and interacting with data using RESTful web services. The ISO 11783 standard series, for instance, defines protocols and messages for agricultural machinery communication, providing a foundation for interoperable irrigation systems [24].