## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Collection to Cloud: IoT Architecture for Smart Irrigation
#### 3.1.1 IoT Devices and Automation of Data Collection  
The automation of data collection through Internet of Things (IoT) technologies is a critical component in developing real-time, end-to-end automated irrigation management systems. IoT devices and structures enable the efficient gathering of vital information about crop health, environmental conditions, and water requirements, essential for enhancing agricultural water use efficiency and crop productivity. IoT devices commonly used in irrigation management include soil moisture sensors, weather stations, and flow meters. These devices are often integrated into wireless sensor networks (WSNs) with energy-efficient communication protocols, offering a cost-effective and scalable solution for real-time data collection in large-scale irrigation systems [51]. For instance, [52] demonstrated an automated irrigation control system using WSNs that efficiently manages water in cultivated fields, improving network lifetime and reducing water consumption.  
Advanced sensing technologies, such as hyperspectral imaging and thermal sensing, have emerged as powerful tools for non-invasive plant stress detection within IoT frameworks. These technologies provide valuable information about crop traits, enabling the early and accurate detection of plant health issues [53]. In a study by [PERSON] et al. [54], a modified TSEB model was used to highlight the importance of high-resolution data in accurately capturing the spatial and temporal dynamics of energy fluxes influenced by environmental factors. Recent advances in satellite technology have significantly improved remote sensing capabilities for irrigation management. Commercial providers now offer daily satellite imagery at a 3-m resolution, suitable for smallholder irrigation applications [55]. This development has influenced the balance between satellite and UAV-based solutions. The irrigation management industry is exploring solutions based on satellite and weather data, potentially reducing reliance on ground truth sensors in some applications [56]. Satellite-based irrigation management solutions have shown agreement with ground-based measurements in recent studies [57]. Despite these advances, IoT-enabled tools such as unmanned aerial vehicles (UAVs) can still offer higher spatial and temporal resolution in specific scenarios, providing a more granular perspective on crop health and development [58; 59]. [PERSON] et al. [60] exemplifies the potential of this approach, demonstrating the use of hyperspectral remote sensing and machine learning to predict leaf water content in rice plants, offering valuable information for optimizing irrigation strategies and improving water use efficiency.  
The integration of IoT devices with cloud computing and edge processing has shown potential to enhance the capabilities of automated irrigation systems. For example, [PERSON] and [PERSON] [61] proposed an IoT-enabled edge computing model for smart irrigation systems, which reported improved performance in terms of latency, bandwidth, throughput, and response time compared to cloud only solutions in their specific experimental setup. These IoT-based systems aim to improve data transmission reliability in challenging environments and may extend the lifespan of sensor nodes. They also offer remote monitoring and automated control capabilities, potentially allowing for more timely adjustments to irrigation schedules based on current field conditions [62].