## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models  
Machine learning (ML) models optimize irrigation management through tasks like soil moisture prediction and crop evapotranspiration estimation. However, challenges persist in dataset quality and model transferability. [PERSON] et al. (2019) partially addressed this by integrating satellite imagery and infield sensors for evapotranspiration prediction (RMSE: 0.11-0.31 mm d\({}^{-1}\)), but noted limitations in obtaining fine-resolution spatiotemporal data. The adaptability of ML models across different agricultural environments presents both challenges and opportunities. Recent research has demonstrated that spatiotemporal modeling approaches can achieve consistent performance across varying conditions, with prediction accuracies of 90% for soil moisture content across different soil depths and types when integrating multiple environmental variables ([PERSON] et al., 2019). This adaptability can be further enhanced through robust decision-making frameworks that explicitly account for environmental uncertainties. For instance, ensemble approaches combining random forests, support vector machines, and neural networks have maintained high performance (R\({}^{2}\) > 0.87) across different environmental conditions by leveraging the complementary strengths of each method ([PERSON] et al., 2019). These advances in model adaptation are particularly valuable for irrigation management, where environmental conditions can vary significantly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Description** & **Benefits** & **Citations** \\ \hline \multirow{4}{*}{Docker} & Package applications and their & \multirow{4}{*}{Enables efficient resource utilization, isolation, and portability of applications.} & \multirow{4}{*}{[89]} \\  & dependencies into self-contained units & & \\  & (containers) for consistent and & & \\  & reproducible execution across & & \\  & different platforms. & & \\ \hline \multirow{4}{*}{Kubernetes} & Manages the deployment, scaling, and & \multirow{4}{*}{Facilitates scalability, automation, and } & \multirow{4}{*}{[90]} \\  & networking of containers across a & & \\  & cluster of machines. & & \\ \hline \multirow{4}{*}{Auto-scaling} & Automatically adjusts the number of & \multirow{4}{*}{Enuses sufficient resources are } \\  & container instances based on & & \\  & real-time demand. & & \\ \hline \multirow{4}{*}{Dynamic Resource Allocation} & Fine-grained adjustment of resources & \multirow{4}{*}{Optimizes resource utilization and } & \multirow{4}{*}{[92]} \\  & allocated to each container based on its & & \\ \cline{1-1}  & specific needs and the & & \\ \cline{1-1}  & current workload. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key technologies and their benefits for containerization. both spatially and temporally. The key to improving model adaptability lies in comprehensive environmental monitoring, continuous model updating based on new observations, and careful validation across different agricultural contexts. This approach ensures that ML models can maintain reliable performance while adapting to the diverse conditions encountered in real-world agricultural settings. Despite these challenges, progress is being made. [PERSON] et al. [96] demonstrated the effectiveness of long short-term memory (LSTM) networks in irrigation scheduling for maize crops. Their hybrid LSTM approach, incorporating data from soil sensors, weather stations, and satellite imagery, achieved high accuracy in predicting soil moisture content reduction (R\({}^{2}\) values: 0.7602-0.9181) across different field conditions. This application showcases the potential of ML models to integrate diverse data sources and provide precise irrigation recommendations, while highlighting ongoing efforts to address transferability issues. In the realm of crop evapotranspiration prediction, [PERSON] et al.