Single Probe Imitation of Multi-Depth Capacitive Soil Moisture Sensor Using Bidirectional Recurrent Neural Network

Abstract

This work proposes a single probe imitation of multidepth capacitive soil moisture sensor for low-cost and energy-efficient implementation of IoT-assisted wireless sensor network (IoWSN) farm monitoring infrastructure. A conditioning circuit (CC) is devised to capture the behavior of soil water movement and its impact on soil moisture around the probe at different depths. The captured correlation is used to train the proposed neural network (NN) models to estimate the soil water content (SWC) at different soil depths based on a single measurement taken at the reference depth. To adjust the weight of the neurons, training and test dataset are collected through a measurement campaign. The data are collected during the cropping period of paddy vegetation by deploying 150 sensor nodes at different depths in bare land before sowing. Two NN models—artificial neural network (ANN) and bidirectional long short term memory network (BLSTM)—are proposed and compared based on the accuracy of SWC estimation. To demonstrate the efficacy of the concept, the proposed sensor design is compared with relevant soil moisture sensors reported over the past five years. The root mean square error (RMSE), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R^{2}}$ </tex-math></inline-formula> and mean absolute percentage error (MAPE)-based analysis validate the significance of the proposed NN models.