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Monitoring crop fields to map features like weeds can be efficientlyperformed with unmanned aerial vehicles (UAVs) that can cover large areas in ashort time due to their privileged perspective and motion speed. However, theneed for high-resolution images for precise classification of features (e.g.,detecting even the smallest weeds in the field) contrasts with the limitedpayload and ight time of current UAVs. Thus, it requires several flights tocover a large field uniformly. However, the assumption that the whole fieldmust be observed with the same precision is unnecessary when features areheterogeneously distributed, like weeds appearing in patches over the field. Inthis case, an adaptive approach that focuses only on relevant areas can performbetter, especially when multiple UAVs are employed simultaneously. Leveragingon a swarm-robotics approach, we propose a monitoring and mapping strategy thatadaptively chooses the target areas based on the expected information gain,which measures the potential for uncertainty reduction due to furtherobservations. The proposed strategy scales well with group size and leads tosmaller mapping errors than optimal pre-planned monitoring approaches.

Monitoring and mapping of crop fields with UAV swarms based on information gain