Abstract
In this work, laboratory based virtual instrumentation (VI) system for recalibration of various smart soil moisture sensors is implemented along with modelling and performance analysis of these sensors. This semiautomatic VI system integrates the standard gravimetric method with wireless data acquisition device to ascertain gravimetric water content in the field soil and online acquisition and analysis of the corresponding voltage samples of the sensors. The proposed VI system, is effectively used to test and record the recalibration data of commercially available analogue groove resistive sensor and capacitive (V1.0) soil moisture sensors and devise efficient characteristics models for the same. Both least-square linear-fit and polynomial-fit inverse models reveal that non-linear model of capacitive soil-moisture sensor has high accuracy (R2 = 0.98) as compared to resistive soil moisture sensor (R2 = 0.96). Further, to enhance measurement accuracy of capacitive soil moisture sensor, 3-layer neural network is modelled using back propagation algorithm to describe the output-input relationship of capacitive senor providing highest value of accuracy (R2 = 0.99). Easy to use and operate with remote connectivity, the VI tool offers ready to use on-site system for recalibration, modelling and comparison of many smart soil moisture sensors for reliable use in IoT based irrigation and water management.
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