  Soil moisture monitoring is a fundamental process to enhance agricultural
outcomes and to protect the environment. The traditional methods for measuring
moisture content in soil are laborious and expensive, and therefore there is a
growing interest in developing sensors and technologies which can reduce the
effort and costs. In this work, we propose to use an autonomous mobile robot
equipped with a state-of-the-art non-contact soil moisture sensor that builds
moisture maps on the fly and automatically selects the most optimal sampling
locations. The robot is guided by an autonomous exploration strategy driven by
the quality of the soil moisture model which indicates areas of the field where
the information is less precise. The sensor model follows the Poisson
distribution and we demonstrate how to integrate such measurements into the
kriging framework. We also investigate a range of different exploration
strategies and assess their usefulness through a set of evaluation experiments
based on real soil moisture data collected from two different fields. We
demonstrate the benefits of using the adaptive measurement interval and
adaptive sampling strategies for building better quality soil moisture models.
The presented method is general and can be applied to other scenarios where the
measured phenomena directly affects the acquisition time and needs to be
spatially mapped.
