  In the last years, unmanned aerial vehicles are becoming a reality in the
context of precision agriculture, mainly for monitoring, patrolling and remote
sensing tasks, but also for 3D map reconstruction. In this paper, we present an
innovative approach where a fleet of unmanned aerial vehicles is exploited to
perform remote sensing tasks over an apple orchard for reconstructing a 3D map
of the field, formulating the covering control problem to combine the position
of a monitoring target and the viewing angle. Moreover, the objective function
of the controller is defined by an importance index, which has been computed
from a multi-spectral map of the field, obtained by a preliminary flight, using
a semantic interpretation scheme based on a convolutional neural network. This
objective function is then updated according to the history of the past
coverage states, thus allowing the drones to take situation-adaptive actions.
The effectiveness of the proposed covering control strategy has been validated
through simulations on a Robot Operating System.
