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 ($\Omega$) and volumetric soil water content (θ). The $\Omega$–$\theta$ 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 $\Omega$–$\theta$ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (EC_s) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (EC_w). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (EC_b) 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.

Core Ideas: Soil–water–disease interactions were analyzed using field and numerical studies. ERT was able to image the soil moisture front separated by active roots. HYDRUS overpredicted RWU from diseased trees, requiring a compensation mechanism. Parameters defining the root distribution were optimized considering plant health. Variably saturated flow models that simulate soil–water–plant interactions within the rhizosphere largely ignore the effect of plant health. This makes it difficult for them to effectively simulate root water uptake (RWU) and implement alternate management practices for diseased trees. This research was aimed at understanding the hydrological and plant controls on RWU simulations dominated by the health of the tree using experimental and numerical frameworks. Two research plots, one around a healthy mature and the other around a diseased mature ( Phytophthora spp. affected) orange tree ( Citrus reticulate Blanco), in the Vidarbha region of India were considered for our analysis. Three‐dimensional electrical resistivity tomography (ERT) performed at the two locations revealed that the soil moisture profiles following irrigation are different between the two plots. A two‐dimensional axisymmetric form of Richards' equation was then solved using HYDRUS (2D/3D) by incorporating a root distribution function. A global sensitivity analysis was performed to identify the root distribution parameters that influence soil moisture simulations. These parameters were then optimized using a genetic algorithm for healthy and diseased conditions. We observed that the diseased orange tree had consumed less water, leaving high soil moisture in the rhizosphere, a condition favorable for the further growth of disease‐causing fungi. Hence, managing irrigation water in accordance with RWU patterns is essential for diseased trees. To understand the error propagation in RWU estimation, we simulated RWU from a diseased tree with the optimal parameters derived for a healthy case. Results show that the error in estimating RWU from the model cells progressively increases with radial distance and soil depth.