发表机构
National Cheng Kung University（国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于视觉语言模型的上下文感知自适应喷洒框架，结合MPPI轨迹跟踪控制，使农业机器人能根据天气、地形和作物信息动态调整喷洒策略，提高作物行检测准确率至少30%，并减少农药漂移。
AI 中文摘要
精准农药喷洒对于优化施药效率和确保化学药剂均匀分布至关重要。喷洒性能受多种因素影响，包括环境条件（如温度和风速）、农药类型，以及机器人准确感知作物和定位喷洒目标的能力。现有方法主要侧重于作物检测，并依赖预定义的喷洒参数，而人类操作员会通过考虑环境条件、区域特定作物特征以及所施农药类型来动态调整其喷洒策略。在本研究中，我们提出了一种基于视觉语言模型（VLMs）的上下文感知自适应喷洒框架，使机器人能够利用空间推理并整合多种来源的信息，包括作物类型、农药类型和天气数据，以做出自适应且优化的喷洒决策。随后，采用基于模型预测路径积分（MPPI）控制的轨迹跟踪控制器，以确保在作物位置进行精确导航和准确喷洒。对比结果表明，所提方法在检测作物行方面将准确率提高了至少30%。此外，在两个环境中进行的实验评估进一步证明了机器人能够灵活调整喷洒量和行进速度，同时减少农药漂移。
英文摘要
Precision pesticide spraying is essential for optimizing application efficiency and ensuring uniform chemical distribution. Spraying performance is influenced by multiple factors, including environmental conditions such as temperature and wind speed, pesticide type, and the robot's capability to accurately perceive crops and target spray locations. Existing approaches predominantly emphasize crop detection and rely on predefined spraying parameters, whereas human operators dynamically adjust their spraying strategies by considering environmental conditions, region-specific crop characteristics, and the type of pesticide being applied. In this study, we propose a context-aware adaptive spraying framework based on Vision-Language Models (VLMs), which enables robots to leverage spatial reasoning and integrate information from multiple sources, including crop type, pesticide type, and weather data, to make adaptive and optimized spraying decisions. Subsequently, a trajectory-tracking controller based on Model Predictive Path Integral (MPPI) control is employed to ensure precise navigation and accurate spraying at crop locations. The comparative results demonstrate that the proposed method improves accuracy by at least 30% in detecting crop rows. In addition, the experimental evaluations conducted in two environments further demonstrate the robot's ability to flexibly adjust spraying volume and travel speed, while reducing pesticide drift.
CommentsAccepted for publication in Computers and Electronics in Agriculture
Journal refComputers and Electronics in Agriculture, Volume 252 (2026) 112092
DOI：10.1016/j.compag.2026.112092
