## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Collection to Cloud: IoT Architecture for Smart Irrigation
By integrating these diverse data types and derived indices, IoT-based smart irrigation systems enable real-time monitoring and data-driven decision-making. Continuously analyzing sensor data and calculating various stress indices allows these systems to provide timely irrigation recommendations, aiming to reduce water waste and optimize crop yield. Furthermore, the historical data collected can be used to refine irrigation strategies over time, adapting to specific crop needs and local environmental conditions. This integration of diverse data types and derived indices represents a promising approach to advancing precision agriculture and enabling more efficient and sustainable water management practices. To ensure the effectiveness of these smart irrigation systems, the continuous monitoring of field conditions and the irrigation infrastructure is crucial. A significant challenge in implementing such systems is the limited availability of reliable internet connectivity in rural agricultural settings, which can be a major barrier to the adoption of advanced irrigation technologies. The recent research has addressed this challenge through systems designed specifically for low-connectivity environments. Modern automated irrigation systems can maintain core functionality during internet outages through local control features and edge computing, with systems capable of making autonomous irrigation decisions based on soil moisture sensor readings even without cloud connectivity [40]. These systems can operate in automatic mode by utilizing historical weather data for rain prediction and maintaining operation through scheduled irrigation even during connectivity issues [41]. Within this framework, IoT sensor networks, computer vision, and multispectral and hyperspectral imaging provide real-time data on soil moisture, temperature, humidity, and crop health [42]. The efficiency of data transmission and access to information is facilitated by edge computing and local processing capabilities, allowing systems to function independently when internet access is limited. This approach enables farmers to maintain reliable irrigation control even in areas with poor connectivity, with systems designed to store and process data locally until network access is restored [40]. Additionally, novel techniques such as entropy-based measures and data visualization offer richer insights into the overall system's health [43]. However, sensor errors and drift can impact the data accuracy and reliability. To mitigate these issues, best practices for sensor calibration and maintenance, such as regular cleaning, reference measurements, and calibration curves, are essential for ensuring data quality [44]. Advanced techniques for data quality control and assurance, like outlier detection, data interpolation, and sensor drift compensation, can be applied in real-time irrigation monitoring and decision support systems [45]. Moreover, data standards and interoperability, such as SensorML [46], AgGateway's ADAPT (ANSI/ASABE S632 [47]) framework, and OGC's SensorThings API, enable effective monitoring and ensure system reliability [46]. Metadata and data provenance, aligned with relevant standards (e.g.,ISO 19115-1:2014 [48]), are also essential for ensuring the traceability, interpretability, and trustworthiness of irrigation monitoring data [49; 50].