The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligence, to improve the agricultural landscape using a network of devices to automatically monitor farmlands. Surveying a large area requires performing a lot of image classification tasks within a specific period of time in order to prevent damage to the farm in case of an incident, such as fire or flood. UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time. Hence, it is assumed that the UAVs are able to partially offload their workload to nearby multi-access edge computing devices. The UAVs need a decision-making algorithm that will decide where the tasks will be performed, while also considering the time constraints and energy level of the other UAVs in the network. In this paper, we introduce a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The proposed method is compared with Q-Learning and three heuristic baselines, and the simulation results show that our proposed DQL-based method achieves comparable results when it comes to the UAVs' remaining battery levels and percentage of deadline violations. In addition, our method is able to reach convergence 13 times faster than Q-Learning.

翻译：第五代和第六代无线通信网络是辅助工具,如物用装置互联网、无人驾驶飞行器和人工智能等,用一个自动监测农田的装置网络改善农业景观,对大面积地区进行勘测需要在一个特定时期内执行许多图像分类任务,以防止发生火灾或洪水等事件时对农场造成损害。无人驾驶飞行器的能量和计算力有限,可能无法在当地和适当时间内执行所有强烈图像分类任务。因此,假设无人驾驶飞行器能够将工作量部分卸下到附近的多接入边缘计算装置。无人驾驶飞行器需要一种决策算法,以决定任务将在哪里执行,同时考虑网络中其他无人驾驶飞行器的时间限制和能量水平。在本文件中,我们采用深Q-学习(DQL)方法来解决这一多目标问题。拟议方法与Q-学习和三个超值基线相比较,并且模拟结果显示,我们提议的DQL-L方法在达到13个最接近的截止日期时,将达到比U-L方法更快的比例。