Volumetric Water Content (VWC) is used for determining the field capacity of any soil. Sensor nodes equipped with VWC and temperature sensor are deployed underground to understand the properties of soil for agricultural activities. The cost of VWC sensors becomes the bottleneck in the deployment of these sensors over a large field. In this paper, we analyze the use of low-cost moisture and temperature sensors to estimate the VWC values and field capacity of any field using machine learning techniques i.e. Artificial Neural Network and Random Forests.