The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency.

使用无人机收集信息并检测被网覆盖的果园中的虫子是一个具有挑战性的问题。这些蚊帐有助于减少害虫损害，但它们也限制了无人机的飞行路径，使其更长、更复杂。为了解决这个问题，我们将果园建模为过道图，这是一种常规数据结构，表示树木排列成直线的连续过道。无人机飞近树木并在特定位置拍照以监测虫子的存在，但其能量有限，因此只能访问一部分位置。为了应对这一挑战，我们引入了单无人机定向航道图问题（SOAP），这是定向问题的一种变体，其中通过为可能受感染的位置分配更大的利润来优先考虑它们。此外，无人机的运动会产生能源成本，目标是在给定无人机电池的情况下，在最有利可图的位置规划无人机的路线。我们证明 SOAP 可以在多项式时间内得到最优求解，但对于较大的果园/实例，我们提出更快的近似和启发式算法。最后，我们在合成数据集和真实数据集上评估算法，以证明其有效性和效率。