  This paper deals with the problem of informative path planning for a UAV
deployed for precision agriculture applications. First, we observe that the
``fear of missing out'' data lead to uniform, conservative scanning policies
over the whole agricultural field. Consequently, employing a non-uniform
scanning approach can mitigate the expenditure of time in areas with minimal or
negligible real value, while ensuring heightened precision in information-dense
regions. Turning to the available informative path planning methodologies, we
discern that certain methods entail intensive computational requirements, while
others necessitate training on an ideal world simulator. To address the
aforementioned issues, we propose an active sensing coverage path planning
approach, named OverFOMO, that regulates the speed of the UAV in accordance
with both the relative quantity of the identified classes, i.e. crops and
weeds, and the confidence level of such detections. To identify these
instances, a robust Deep Learning segmentation model is deployed. The
computational needs of the proposed algorithm are independent of the size of
the agricultural field, rendering its applicability on modern UAVs quite
straightforward. The proposed algorithm was evaluated with a simu-realistic
pipeline, combining data from real UAV missions and the high-fidelity dynamics
of AirSim simulator, showcasing its performance improvements over the
established state of affairs for this type of missions. An open-source
implementation of the algorithm and the evaluation pipeline is also available:
\url{https://github.com/emmarapt/OverFOMO}.
