## 2 Review Methodology

Table 1: Research questions and hypotheses framework for analyzing automated irrigation systems. \begin{table}
\begin{tabular}{c c c c c} \hline
**Objective** & **Specific Objective** & **Research Question** & **Motivation** & **Hypothesis** \\ \hline \multirow{6}{*}{Data Processing and Decision Making in the Cloud} & What machine learning algorithms and cloud-based platforms are being utilized for predictive modeling and irrigation computing for irrigation management. & To investigate the current trends and cloud-based platforms are being utilized for learning and cloud computing for irrigation management. & Deep learning models and cloud-based platforms are cloud-based platforms are increasingly adopted for accurate and adaptive computing for irrigation scheduling in automated systems. \\ \hline \multirow{6}{*}{Data Processing and Decision Making in the Cloud} & How are online learning algorithms and real-time data integration enabling adaptive and dynamic irrigation management? & To explore the role of online learning and real-time data & Online learning algorithms enable automated irrigation systems to continuously adapt to changing conditions and optimize water use efficiency. \\ \hline \multirow{6}{*}{Irrigation Scheduling and Application} & How are ML-generated insights being translated into actionable irrigation commands and applied to various irrigation systems (e.g., sprinkler, drip, surface)? & To examine the effectiveness of automated systems in controlling different types of irrigation infrastructure. & Automated irrigation systems utilizing ML-generated insights can effectively control various irrigation technologies, optimizing water delivery and minimizing water. \\ \hline \multirow{6}{*}{What closed-loop control techniques are being employed to ensure precise and adaptive irrigation based on real-time feedback?} & What closed-loop control techniques, such as PID controllers and model of closed-loop control in achieving dynamic and responsive irrigation management. & Closed-loop control techniques, such as PID controllers and model predictive control, are crucial for achieving precise and adaptive irrigation based on real-time feedback. \\ \hline \multirow{6}{*}{Analyze the Effectiveness and Efficiency of Integrated End-to-End Automated Irrigation Systems} & What are the documented benefits and limitations of fully autonomous irrigation systems in terms of water use efficiency, crop yield, water use efficiency, crop yield, and labor productivity? & To assess the overall irrigation systems can significantly improve water use efficiency, crop yield, and labor productivity compared to traditional irrigation methods. \\ \hline \multirow{6}{*}{Investigate the Role of Interoperability and Standardization} & What are the key challenges and opportunities for achieving interoperability and standardization in automated irrigation systems? & To identify the barriers and potential solutions & The lack of standardized data formats and communication protocols \\ \hline \multirow{6}{*}{What research gaps and Identify Gaps and In Groupes Solutions for Seamless Integration & To outline the future research needs and potential advancements in the field of automated irrigation management, addressing challenges related to data quality, security, and interoperability. \\ \cline{1-1}  & & & Future research should focus on developing more robust, scalable, and cost-effective solutions for automated irrigation management, addressing challenges related to data quality, security, and interoperability. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 1: Flowchart depicting the literature search and selection process.