基于深度学习的智能流量控制梨园喷雾机器人田间评价
Field Evaluations of A Deep Learning-based Intelligent Spraying Robot with Flow Control for Pear Orchards
摘要：
本文提出了一种可变流量控制系统的实时的与深度学习使用分割的果树在梨园。实时的的流量控制、不期望的压力波动和理论建模可能与现实世界中的那些不同。因此，设计了两种类型的初步实验来检查流量建模的线性关系。通过初步试验，优化了脉宽调制（PWM）控制器的参数，并进行了实际的现场试验，验证了变流量控制系统的性能。田间试验结果表明，该系统的性能是令人满意的，因为它表明，它可以减少农药的使用和农药暴露的风险。特别是，由于田间试验是在非结构化环境中进行的，所提出的变流控制系统预计将充分适用于其他果园。
英文摘要：
This paper proposes a variable flow control system in real time with deep learning using the segmentation of fruit trees in a pear orchard. The flow rate control in real time, undesired pressure fluctuation and theoretical modeling may differ from those in the real world. Therefore, two types of preliminary experiments were designed to examine the linear relationship of the flow rate modeling. Through a preliminary experiment, the parameters of the pulse width modulation (PWM) controller were optimized, and an actual field experiment was conducted to confirm the performance of the variable flow rate control system. As a result of the field experiment, the performance of the proposed system was satisfactory, as it showed that it could reduce pesticide use and the risk of pesticide exposure. Especially, since the field experiment was conducted in an unstructured environment, the proposed variable flow control system is expected to be sufficiently applicable to other orchards.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
