## 2 Review Methodology  
In this review, a comprehensive, multi-stage methodology is employed to identify, evaluate, and synthesize the research on real-time, autonomous irrigation management systems. The review process, shown in Figure 1, permits a detailed analysis of the current developments in this field. The literature search encompassed multiple scientific databases, including Google Scholar, Scopus, and Web of Science, using a targeted set of search terms such as'smart irrigation', 'IoT irrigation', 'automated irrigation', and 'precision irrigation'. The search included academic research papers, relevant standards and guidelines from various organizations, and studies from adjacent fields that could inform irrigation management practices. This broader scope captured the latest scientific findings, and established best practices, industry benchmarks, and insights from related domains such as precision agriculture, remote sensing, machine learning, and IoT technologies. The literature review began with a database keyword search yielding 1485 potential papers. Title and abstract screening using predefined criteria reduced this to 462 papers for full-text review. Stringent evaluation focusing on detailed system architectures, data processing, control algorithms, and performance metrics initially selected 123 papers. Throughout this review, additional relevant works were identified via supplementary searches, citations, and expert recommendations. These underwent the same rigorous evaluation, with valuable additions incorporated. This iterative process culminated in a final selection of 166 papers, standards, and resources, ensuring a comprehensive, up-to-date review while maintaining high quality and relevance standards. A question-driven framework, centered on the key research questions and hypotheses (Table 1), guided the analysis of the selected literature. These questions addressed the overarching objective of critically evaluating the current state and future potential of real-time, end-to-end automated irrigation management systems while considering contributions and implications from adjacent fields such as networking, machine learning, cybersecurity and high-performance computing and the role of industry standards and guidelines. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Specific Objective** & **Research Question** & **Motivation** & **Hypothesis** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Examine the \\ Automation of the \\ Irrigation \\ Management Pipeline \\ \end{tabular} } & \begin{tabular}{c} What IoT devices and \\ communication \\ protocols are most \\ effective for real-time \\ to Cloud \\ \end{tabular} & \begin{tabular}{c} To understand the \\ current state-of-the-art \\ effective for real-time \\ data collection and \\ transmission in diverse \\ agricultural \\ environments? \\ \end{tabular} & \begin{tabular}{c} Low-power wide-area \\ networks (LPWANs) and \\ edge computing are \\ increasingly prevalent for \\ efficient and reliable data \\ collection and \\ mitigation management. \\ \end{tabular} \\ \hline \multirow{6}{*}{\begin{tabular}{c} \end{tabular} } & How are data quality & \multirow{6}{*}{\begin{tabular}{c} To assess the \\ effectiveness of data \\ quality control and \\ preprocessing methods \\ in ensuring reliable \\ data for irrigation \\ systems? \\ \end{tabular} } \\  & \begin{tabular}{c} How are data quality \\ and preprocessing \\ techniques ensuring \\ accurate and reliable \\ data for \\ decision-making in \\ automated irrigation \\ systems? \\ \end{tabular} & \
\begin{tabular}{c} To assess the \\ effectiveness of data \\ quality control and \\ preprocessing methods \\ in ensuring reliable \\ data for irrigation \\ management. \\ \end{tabular} &
\begin{tabular}{c} Advanced data filtering, \\ outlier detection, and \\ imputation techniques \\ significantly improve the \\ accuracy and reliability of \\ data used for irrigation \\ decision-making. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}