  Monitoring crop fields to map features like weeds can be efficiently
performed with unmanned aerial vehicles (UAVs) that can cover large areas in a
short time due to their privileged perspective and motion speed. However, the
need for high-resolution images for precise classification of features (e.g.,
detecting even the smallest weeds in the field) contrasts with the limited
payload and ight time of current UAVs. Thus, it requires several flights to
cover a large field uniformly. However, the assumption that the whole field
must be observed with the same precision is unnecessary when features are
heterogeneously distributed, like weeds appearing in patches over the field. In
this case, an adaptive approach that focuses only on relevant areas can perform
better, especially when multiple UAVs are employed simultaneously. Leveraging
on a swarm-robotics approach, we propose a monitoring and mapping strategy that
adaptively chooses the target areas based on the expected information gain,
which measures the potential for uncertainty reduction due to further
observations. The proposed strategy scales well with group size and leads to
smaller mapping errors than optimal pre-planned monitoring approaches.
