## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models
#### 4.3.1 Deploying ML Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Framework** & **Description** & **Benefits** & **Citations** \\ \hline \multirow{3}{*}{Serving} & \multirow{3}{*}{High-performance system for serving} & Efficient and scalable inference, suitable & \multirow{3}{*}{[99]} \\  & & & for real-time applications with low \\  & & TensorFlow models. & latency and high & \\  & & & throughput requirements. & \\ \hline \multirow{6}{*}{Apache MXNet Model Server} & Flexible and efficient solution for & \multirow{3}{*}{Versatility and support for various} & \multirow{3}{*}{[100]} \\  & deploying models trained with MXNet, & & deep learning models, suitable for \\  & supporting a wide range of deep & \multirow{3}{*}{complex irrigation systems utilizing} & \multirow{3}{*}{different types of ML models.} \\  & learning models and & & \\  & inference backends. & & \\ \hline \multirow{6}{*}{ONNX Runtime} & Cross-platform inference engine & Enables deployment of models in & \multirow{3}{*}{[101]} \\  & compatible with various ML & & diverse environments, facilitating \\  & frameworks, including PyTorch, & interoperability and reducing the need & \\  & TensorFlow, and MXNet. & & for model conversion. \\ \hline \multirow{6}{*}{Model Compression} & Techniques such as pruning and & \multirow{3}{*}{Improves model efficiency and reduces} & \multirow{3}{*}{[102]} \\  & quantization to reduce the size and & & resource consumption, making it \\  & computational requirements of ML & & suitable for resource-constrained \\  & models without & environments or real-time applications. & \\  & compromising accuracy. & & \\ \hline \multirow{6}{*}{Hardware Acceleration} & Utilizes GPUs or TPUs to significantly & \multirow{3}{*}{Reduces inference time and enables} & \multirow{3}{*}{[103]} \\  & enhance model performance by & & real-time processing of sensor data for \\  & leveraging specialized hardware & & timely irrigation decisions. & \\ \hline \multirow{6}{*}{Distributed Training} & Techniques like Horovod and BytePS to & Enables training of complex models & \multirow{3}{*}{[104]} \\  & distribute training across multiple & & with large datasets in a shorter time, \\  & machines, parallelizing the process and & & improving model accuracy \\  & reducing training time. & & and efficiency. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Technologies, frameworks, and optimization techniques for deploying ML. The integration of IoT sensor data with cloud-based data sources is crucial for enhancing the accuracy and resolution of irrigation insights. Data fusion techniques, such as Kalman filtering, play a critical role in combining heterogeneous data sources to generate accurate and comprehensive irrigation recommendations by integrating real-time sensor data with broader spatial and temporal information [105]. Advanced machine learning approaches, like transfer learning with deep neural networks, further improve the accuracy of predictions for key variables like soil moisture content [106]. Various machine learning approaches, each with unique strengths and limitations, have been successfully applied for generating irrigation insights. Fuzzy logic, for instance,  
Figure 5: Automated data processing pipeline in the cloud for irrigation management. handles uncertainty and vagueness in decision-making using linguistic variables and rules, offering interpretability and the ability to incorporate expert knowledge [107]. Deep learning models, like LSTM networks and CNNs, excel at capturing complex spatiotemporal dependencies and enabling end-to-end learning, but typically require large, labeled datasets [108]. Reinforcement learning approaches learn optimal irrigation policies through trial-and-error interactions with the environment, adapting well to dynamic conditions but requiring careful design of state-action spaces and reward functions [109].