Efficient crop production requires early detection of pest outbreaks and timely treatments; we consider a solution based on a fleet of multiple autonomous miniaturized unmanned aerial vehicles (nano-UAVs) to visually detect pests and a single slower heavy vehicle that visits the detected outbreaks to deliver treatments. To cope with the extreme limitations aboard nano-UAVs, e.g., low-resolution sensors and sub-100 mW computational power budget, we design, fine-tune, and optimize a tiny image-based convolutional neural network (CNN) for pest detection. Despite the small size of our CNN (i.e., 0.58 GOps/inference), on our dataset, it scores a mean average precision (mAP) of 0.79 in detecting harmful bugs, i.e., 14% lower mAP but 32x fewer operations than the best-performing CNN in the literature. Our CNN runs in real-time at 6.8 frame/s, requiring 33 mW on a GWT GAP9 System-on-Chip aboard a Crazyflie nano-UAV. Then, to cope with in-field unexpected obstacles, we leverage a global+local path planner based on the A* algorithm. The global path planner determines the best route for the nano-UAV to sweep the entire area, while the local one runs up to 50 Hz aboard our nano-UAV and prevents collision by adjusting the short-distance path. Finally, we demonstrate with in-simulator experiments that once a 25 nano-UAVs fleet has combed a 200x200 m vineyard, collected information can be used to plan the best path for the tractor, visiting all and only required hotspots. In this scenario, our efficient transportation system, compared to a traditional single-ground vehicle performing both inspection and treatment, can save up to 20 h working time.

翻译：高效作物生产需要及早发现病虫害爆发并及时处理；我们提出一种基于多架自主微型无人机（纳米无人机）进行视觉虫害检测，并由单辆较慢的重型车辆前往检测到的爆发点投放药剂的解决方案。为应对纳米无人机上的极端限制（例如低分辨率传感器和低于100 mW的计算功耗预算），我们设计、微调并优化了一个用于虫害检测的微型图像卷积神经网络（CNN）。尽管我们的CNN规模很小（即每次推理0.58 G运算），但在我们的数据集上，其检测有害昆虫的平均精度均值（mAP）达到0.79，即比文献中性能最佳的CNN低14%的mAP，但运算量减少32倍。我们的CNN在Crazyflie纳米无人机搭载的GWT GAP9片上系统上以6.8帧/秒的速度实时运行，功耗为33 mW。其次，为应对田间意外障碍，我们采用了基于A*算法的全局+局部路径规划器。全局路径规划器确定纳米无人机扫描整个区域的最佳路线，而局部规划器以高达50 Hz的频率在纳米无人机上运行，通过调整短距离路径来防止碰撞。最后，我们通过模拟实验证明，一旦由25架纳米无人机组成的机群完成对200x200米葡萄园的梳理，所收集的信息可用于规划拖拉机的最佳路径，使其仅访问所有必要的热点区域。在此场景下，与执行检测和处理双重任务的传统单地面车辆相比，我们的高效运输系统可节省多达20小时的工作时间。