Ma Na, Mantri Anil, Bough Graham, Patnaik Ayush, Yadav Siddhesh, Nansen Christian, Kong Zhaodan
Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States.
Department of Entomology and Nematology, University of California, Davis, Davis, CA, United States.
Front Robot AI. 2022 Aug 10;9:854381. doi: 10.3389/frobt.2022.854381. eCollection 2022.
In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV's movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations.
近几十年来，无人机在农业领域颇受欢迎，基于无人机的作业被用于喷洒农药和释放生物防治剂。这种基于无人机的作业面临的一个关键挑战是要考虑风速和无人机飞行参数，以实现农药和生物防治剂的精准投放最大化。本文描述了一个数据驱动框架，用于预测从悬停无人机喷出的蛭石的密度分布模式，该模式是无人机运动状态、风况和喷洒器设置的函数。通过我们提出的学习算法得出的模型，能够根据训练数据和测试数据准确预测蛭石分布模式。我们的框架和算法可以轻松转换到其他使用不同无人机和喷洒器、针对不同农药和作物的精准害虫管理问题。此外，由于我们的模型具有简单的分析形式，可以将其纳入控制器设计中，该控制器能够优化无人机向多个目标位置自主投放所需数量的捕食螨。
