  In this paper, we introduce an informative path planning (IPP) framework for
active classification using unmanned aerial vehicles (UAVs). Our algorithm uses
a combination of global viewpoint selection and evolutionary optimization to
refine the planned trajectory in continuous 3D space while satisfying dynamic
constraints. Our approach is evaluated on the application of weed detection for
precision agriculture. We model the presence of weeds on farmland using an
occupancy grid and generate adaptive plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We validate our
approach in simulation by comparing against existing methods, and study the
effects of different planning strategies. Our results show that the proposed
algorithm builds maps with over 50% lower entropy compared to traditional
"lawnmower" coverage in the same amount of time. We demonstrate the planning
scheme on a multirotor platform with different artificial farmland set-ups.
