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}.

翻译：本文研究了面向精准农业应用的无人机信息型路径规划问题。首先，我们观察到，“错失恐惧症”会导致对整个农田采用均匀、保守的扫描策略。因此，采用非均匀扫描方法可以减少在真实价值极小或可忽略区域的时间消耗，同时确保在信息密集区域实现高精度。在审视现有信息型路径规划方法时，我们发现某些方法需要大量计算资源，而其他方法则需在理想世界模拟器上进行训练。为解决上述问题，我们提出了一种名为OverFOMO的主动感知覆盖路径规划方法，该方法根据识别类别（即作物与杂草）的相对数量及检测置信度来调节无人机的速度。为识别这些实例，我们部署了鲁棒的深度学习分割模型。所提算法的计算需求与农田面积无关，因此易于应用于现代无人机。通过结合真实无人机任务数据与AirSim高保真动力学模拟的仿真-真实混合管道，对算法进行了评估，展示了其在此类任务中相较于现有技术的性能提升。该算法与评估管道的开源实现也已公开：\url{https://github.com/emmarapt/OverFOMO}。