Improving the spatial deployment of the soil moisture sensors in smart irrigation systems using GIS

Incorporating the Internet of Things (IoT) and smart irrigation systems into developing regions encounters significant financial constraints. To address this gap, this study aimed to identify the most effective locations for the sensor deployment using the Geographic Information System (GIS) techniques, maximizing the spatial coverage of soil moisture states while minimizing the number of required wireless sensor nodes. Ensuring the accuracy of YL-69 soil moisture sensors is pivotal for system efficiency therefore, a volumetric water content (VWC) calibration was conducted. Soil samples from the surface and subsurface layers were subjected to a comprehensive laboratory analysis to assess their physical and chemical attributes. Employing the Soil-Plant-Atmosphere-Water model (SPAW), the available water-holding capacity (AWHC) for these soil samples was estimated. A sensor placement strategy was formulated, aligning with AWHC maps to detect the spatial variations at varying depths. Further soil samples were collected to fine-tune the sensor calibration. Our findings revealed that third-order polynomial regression equations yielded the best correspondence between the sensor readings and the reference VWC measurements, with R2 values ranged from 0.94 to 0.99 for surface layers and 0.95 to 0.98 for subsurface layers. This innovative approach facilitated the deployment of IoT and smart irrigation applications by determining the optimal sensor placement and enhancing the efficiency and cost-effectiveness of the water management systems.