  Recent developments in robotic and sensor hardware make data collection with
mobile robots (ground or aerial) feasible and affordable to a wide population
of users. The newly emergent applications, such as precision agriculture,
weather damage assessment, or personal home security often do not satisfy the
simplifying assumptions made by previous research: the explored areas have
complex shapes and obstacles, multiple phenomena need to be sensed and
estimated simultaneously and the measured quantities might change during
observations. The future progress of path planning and estimation algorithms
requires a new generation of benchmarks that provide representative
environments and scoring methods that capture the demands of these
applications.
  This paper describes the Waterberry Farms benchmark (WBF) that models a
precision agriculture application at a Florida farm growing multiple crop
types. The benchmark captures the dynamic nature of the spread of plant
diseases and variations of soil humidity while the scoring system measures the
performance of a given combination of a movement policy and an information
model estimator. By benchmarking several examples of representative path
planning and estimator algorithms, we demonstrate WBF's ability to provide
insight into their properties and quantify future progress.
