  The amount of water present in soil is measured in terms of a parameter
commonly referred to as Volumetric Water Content (VWC) and is used for
determining the field capacity of any soil. It is an important parameter
accounting for ensuring proper irrigation at plantation sites for farming as
well as for afforestation activities. The current work is an extension to
already going on research in the area of wireless underground sensor networks
(WUSNs). Sensor nodes equipped with Decagon 5TM volumetric water content (VWC)
and temperature sensor are deployed underground to understand the properties of
soil for agricultural activities. The major hindrances in the deployment of
such networks over a large field are the cost of VWC sensors and the
credibility of the data being collected by these sensors. In this paper, we
analyze the use of low-cost moisture and temperature sensors that can either be
used to estimate the VWC values and field capacity or cross-validate the data
of expensive VWC sensors before the actual deployment. Machine learning
algorithms, namely Neural Networks and Random Forests are investigated for
predicting the VWC value from low-cost moisture sensors. Several field
experiments are carried out to examine the proposed hypothesis. The results
showed that low-cost moisture sensors can assist in estimating the VWC and
field capacity with a minor trade-off.
