Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

Abstract

Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity–capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (Ω) and volumetric soil water content (θ). The Ω–θ relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the Ω–θ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (ECs) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (ECw). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (ECb) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks.

Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing

“…Soil samples were collected in 2018 from eight different locations at three different depths: 30, 60, and 90 cm, for soil texture assessment and sensor calibration. Using the gravimetric calibration procedure (Peddinti et al 2020a), the following calibration equation was developed to get calibrated volumetric water content from the sensors: here, volumetric is volumetric water content obtained from the sensors and gravimetric is the gravimetric water content obtained from the soil samples (Table 1).…”

“…However, one disadvantage of the soil moisture sensors is that they require site-specific calibration to provide reliable measurements of volumetric water content. Because of the effects that variations in soil properties have on sensor output, it is necessary to calibrate the sensor at each site (Peddinti et al 2020a). Also, the volume of soil sensed is somewhat limited.…”

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

“…El-kenawy et al [32] evaluate the performance of the hybrid ensemble model in the prediction of daily ET. Srivivosa Peddinti et al [33] assess the impacts of salinity on soil moisture by using sensors for precision agriculture applications. Ramchandran et al [34] propose automated irrigation water solutions using IoT, and cloud computing to conserve irrigation water.…”

Design and fabrication of low-cost capacitive sensors for salinity level detection in water

“…Several methods can be employed to determine the presence of salt in the water such as remote sensing methods (Metternicht and Zinck, 2003) (including multi-temporal optical and microwave signals), optical fiber sensor-based methods (Qian et al, 2018), conductive sensing methods (capacitive or resistive) Research Article (Peddinti et al, 2020), and electromagnetic inductive sensing methods (Wu et al, 2015). Among these techniques, the capacitive type sensing method has been adopted by many researchers due to its low cost and easy fabrication procedures.…”