## 3 Data Collection to Cloud: Automation and Real-Time Processing  
### Data Collection to Cloud: IoT Architecture for Smart Irrigation  
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. In this section, how IoT architectures are implemented in practical irrigation systems is examined, with specific field implementations further detailed in the case studies in Section 7. The evolution of these systems from basic monitoring to fully automated control demonstrates the practical realization of IoT capabilities in agricultural settings. As shown in Figure 2, data collection is the first step in the IoT-based architecture for smart and automated irrigation management. The success of these systems depends on the efficient collection, transmission, and analysis of various data types. Among these, soil moisture data are arguably the most critical, as it directly reflects water availability to plants and informs optimal irrigation timing and amount [25; 26]. Soil moisture sensors, such as tensiometers, capacitance probes, and time-domain reflectometry (TDR) sensors, provide real-time measurements of soil water content at various depths [27]. These measurements enable the calculation of soil water stress indices, like the Soil Water Stress Index (SWSI) [28; 29], and the observation of soil moisture depletion trends. SWSI quantifies the level of water stress experienced by plants based on available soil moisture relative to critical thresholds such as field capacity and permanent wilting point [30]. Such indices provide a normalized measure of soil water stress, potentially ranging from 0 (no stress) to 1 (maximum stress), which can be used to inform precise irrigation scheduling, preventing both over- and under-watering [25]. In addition to soil moisture, canopy temperature data, collected using infrared thermometers and thermal cameras, are valuable for assessing plant water stress and adjusting irrigation [31]. These data can be used to calculate indices like the Crop Water Stress Index  
Figure 2: Automated irrigation system architecture depicting the flow from data collection through IoT devices, transmission via various networks, cloud processing, decision-making, to precision application. (CWSI), which compares actual canopy-air temperature differences to theoretical wet and dry references [32; 33; 34]. However, the interpretation and application of thermal data present significant challenges [35], such as issues in utilizing thermal remote sensing images for crop and soil surface temperature information. While thermal sensors provide reliable spatial and temporal trends, accurate absolute canopy temperature measurements remain challenging, with one study showing a strong correlation (R\({}^{2}\) = 0.89) between low-cost and professional thermal sensors [36]. Moreover, crop stress detection using thermal data is highly time-sensitive, with pre-irrigation thermal images under cloud-free conditions better reflecting water stress than post-irrigation images [37]. These complexities complicate the integration of thermal data into real-time irrigation management systems. Nevertheless, thermal stress indices may be more effective for deficit-irrigated crops, as demonstrated by [PERSON] and [PERSON] [38], who showed that using daily CWSI thresholds for irrigation scheduling in deficit-irrigated grapevines reduced water use without compromising yield or berry composition. To further enhance the irrigation decision-making, advanced machine learning algorithms can be applied to weather data to predict crop evapotranspiration (ET) under specific conditions [33; 39].