## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Transmission: Edge Computing and Fog Computing  
Edge computing and fog computing are being explored as technologies to enhance real-time irrigation management, with the aim of improving efficiency, scalability, and reliability. However, agricultural IoT systems face several critical data processing bottlenecks that must be addressed. Recent analysis has identified specific constraints including data transmission latency due to network congestion, processing power limitations at local gateways, and challenges in real-time analysis of massive sensor datasets [75]. These bottlenecks particularly impact time-sensitive irrigation decisions, where delayed processing can lead to suboptimal water management. Implementation of hybrid edge-cloud architectures has demonstrated promising solutions to these processing challenges. Studies combining edge computing with efficient data management algorithms have achieved significant improvements, including a 30% reduction in data processing time and a 25% improvement in real-time monitoring capabilities through optimized local processing and data transmission [75]. More recent advances using TinyML algorithms at the edge have shown even more dramatic improvements, with processing times decreasing fourfold when scaling from 1 to 10 processing nodes while maintaining 88-95% compression efficiency [76]. These performance gains are particularly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Description** & **Benefits** & **Citations** \\ \hline Filtering & \begin{tabular}{c} Recursively estimates the system’s state \\ based on previous measurements and \\ current sensor data, considering noise \\ and uncertainty. Effective for handling \\ missing values and correcting \\ erroneous readings. \\ \end{tabular} & Improves data accuracy and reliability \\  & \begin{tabular}{c} and uncertainty. Effective for handling \\ missing values and correcting \\ erroneous readings. \\ \end{tabular} & by mitigating the impact of noise and sensor malfunctions. & [69] \\ \hline Moving Average & \begin{tabular}{c} Averages consecutive data points to \\ provide a more stable representation of \\ the underlying trend, filtering out \\ short-term fluctuations. \\ \end{tabular} & \begin{tabular}{c} Smooths out data and reduces the \\ impact of temporary variations, leading \\ to a more accurate representation \\ of trends. \\ \end{tabular} & [70] \\ \hline Adaptive Thresholding & \begin{tabular}{c} Dynamically adjusts thresholds based \\ on the statistical properties of the data \\ to effectively identify anomalies and \\ minimize false positives. \\ \end{tabular} & \begin{tabular}{c} Accurately identifies outliers while \\ minimizing the risk of incorrectly \\ flagging normal data points \\ as anomalies. \\ \end{tabular} & [71] \\ \hline Data Normalization & \begin{tabular}{c} Transforms data values to a common \\ scale (e.g., min–max scaling, z-score \\ normalization) to ensure all features \\ contribute equally to the analysis. \\ \end{tabular} & \begin{tabular}{c} Prevonts features with larger values \\ from dominating the analysis and \\ ensures all features are \\ considered equally. \\ \end{tabular} & [72] \\ \hline Feature Scaling & \begin{tabular}{c} Optimizes the range of feature values \\ to improve the performance and \\ convergence of machine learning \\ models (e.g., standardization, \\ normalization). \\ \end{tabular} & \begin{tabular}{c} Reduces the influence of outliers and \\ enhances the model’s ability to learn \\ from the data, leading to improved \\ accuracy and performance. \\ \end{tabular} & [73] \\ \hline Data Fusion & \
\begin{tabular}{c} Integrates information from diverse \\ sources to create a more comprehensive \\ and reliable dataset. \\ \end{tabular} &
\begin{tabular}{c} Provides a holistic understanding of \\ crop water requirements and \\ environmental conditions by \\ combining data from various sensors \\ and sources, leading to more informed \\ and accurate irrigation decisions. \\ \end{tabular} & [74] \\ \hline \hline \end{tabular}
\end{table}