## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models
#### 4.3.2 Automated Application of Irrigation Insights Using Irrigation Systems  
The integration of ML-generated insights with IoT-enabled irrigation control systems requires the adoption of appropriate architectures and protocols that facilitate lightweight, real-time communication [111; 112]. The message queuing telemetry transport (MQTT) protocol and the constrained application protocol (CoAP) are well-suited for this purpose, offering efficient and reliable communication channels for exchanging data between sensors, actuators, and ML models [114]. Industry-leading products and services for smart irrigation demonstrate the practical application of these technologies. Smart irrigation controllers leverage weather data and soil moisture sensors to automatically adjust irrigation schedules based on real-time conditions, reducing water waste and promoting water conservation [113]. The message queuing telemetry transport (MQTT) protocol and the constrained application protocol (CoAP) are wellsuited for this purpose, offering efficient and reliable communication channels for exchanging data between sensors, actuators, and ML models [114]. Currently, the integration of IoT and machine learning technologies in automated irrigation systems has been applied to various irrigation methods. Center pivot systems, in particular, have evolved significantly beyond basic sprinkler capabilities. Modern pivot systems commonly incorporate multiple advanced technologies: corner arm systems that extend to irrigate field corners typically missed by standard circular patterns; GPS guidance for precise positioning and navigation; variable rate irrigation capabilities that allow different application rates across the field; and individual sprinkler controls for precise water distribution. While drip irrigation systems have seen extensive IoT integration for soil moisture monitoring and automated control [115], utilizing real-time sensor data and predictive models to optimize irrigation scheduling, the extent of adoption and comparative effectiveness across different system types remains an area of ongoing research. As research and development progress, the combination of IoT and machine learning is expected to advance automated irrigation across various system types, enabling more precise and adaptive water management strategies. Different irrigation systems may have varying potential for automation due to factors such as precision, adaptability, and compatibility with advanced sensing and control technologies. [PERSON] et al. [116] demonstrated that smart sprinkler systems can reduce water loss by up to 95% compared to conventional methods, while [PERSON] et al. [117] showed automated drip systems effectively optimize soil moisture conditions. However, comprehensive comparative studies across all irrigation types specifically regarding their automation potential are limited, making it difficult to definitively rank their automation potential. Figure 6 presents an overview of various irrigation technologies, including sprinkler, drip, furrow, and flood irrigation, in terms of their current automation implementations and challenges. of a smart automated furrow system for cotton. Flood irrigation faces certain challenges for full automation, with limited research on successful IoT or ML applications. However, [PERSON] et al. [122] achieved 86.6% irrigation efficiency improvement through an automated soil moisturebased system for basin layout. The implementation of fully automated control varies across these technologies, with each system type presenting unique opportunities and challenges based on their design and water application methods. It is important to note that systems with higher levels of automation also face challenges such as higher initial costs, maintenance requirements, and energy consumption compared to traditional surface irrigation methods.