UAV route planning for active disease classification

Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods.