Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and limited battery capacity of UAVs. To address these issues, we formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it. Furthermore, we propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs and utilizes a mediator Q-network over a double deep Q-network (DDQN) to accelerate and stabilize training and improve performance. Experimental results in both simulated and real-world environments demonstrate the effectiveness of our solution. Moreover, our proposed ITDQN outperforms DDQN by 4.43\% in weed recognition rate and 6.94\% in data collection rate.

翻译：无人机已成为智能农业领域一种极具前景的辅助平台，能够同时执行杂草检测、识别以及从无线传感器收集数据的任务。然而，由于环境的高度不确定性、部分可观测性以及无人机有限的电池容量，基于无人机的智能农业轨迹规划面临挑战。为解决这些问题，我们将轨迹规划问题建模为马尔可夫决策过程，并利用多智能体强化学习进行求解。此外，我们提出了一种新颖的基于模仿的三重深度Q网络算法，该算法采用精英模仿机制以降低探索成本，并在双重深度Q网络之上引入一个中介Q网络，以加速和稳定训练过程并提升性能。在模拟和真实环境中的实验结果验证了我们方案的有效性。此外，我们提出的ITDQN算法在杂草识别率上优于DDQN算法4.43%，在数据收集率上优于6.94%。