外观
论文arXiv 2608.275452 个月前
温室中通过虚拟现实进行远程人机交互：植物冠层结构如何影响叶片病害和土壤湿度检查
原文链接：Remote Human-Robot Interaction In Greenhouses via Virtual Reality: How Plant Canopy Structure Affects Leaf Disease and Soil Moisture Inspection（arXiv 2608.27545 · 发布日期：2026-08-27）
Remote Human-Robot Interaction In Greenhouses via Virtual Reality: How Plant Canopy Structure Affects Leaf Disease and Soil Moisture Inspection
研究用VR遥操作地面车与机械臂在温室检查叶片和土壤湿度，验证冠层遮挡对可靠性的影响。
核心要点
- 面向温室远程叶片病害检查和土壤湿度评估，关注植物冠层结构带来的遮挡影响。
- 系统由带相机的无人地面车和机械臂组成，基于运动学模型进行导航与机械臂控制。
- 两次VR遥操作实验检查14株植物，叶片病害检测准确率最高88%，浇水需求判断最高64.3%。
- 事后分析显示冠层形态显著预测土壤湿度评估可靠性，遮挡是主要限制因素。
验证范围与局限：验证范围为温室环境中14株植物的两次VR遥操作实验，任务包括叶片检查和土壤湿度评估。
英文摘要 (Abstract)
This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a greenhouse environment. The robotic system comprised an unmanned ground vehicle and a robotic manipulator equipped with cameras, governed by kinematic models for navigation and manipulator control. Fourteen distinct plants were inspected across two experiments utilizing VR teleoperation, guided by a set of pre-specified research questions and hypotheses. In the leaf inspection experiments, cycle completion times varied from 3.3 to 8.0 s, and plant-based disease detection was achieved up to 88% accuracy; diseased-spot detection improved numerically in the second experiment, though this change was not statistically significant (p=0.378). For soil moisture assessment, the experiments achieved successful determination of watering needs in up to 64.3% of plants (9 of 14), with consistent success observed for plants 1, 2, 3, 8, 9, 10, and 13; however, this improvement was likewise not statistically significant (p=0.50). A post hoc analysis instead revealed that soil moisture assessment reliability was strongly and significantly predicted by plant canopy morphology (p<0.01): plants with broad, single-leaf canopies reached 100% success by the second experiment, versus only 16.7% for dense, compound canopies. A secondary analysis showed operators became measurably faster at attempting dense-canopy plants without a corresponding gain in success, indicating that camera occlusion, not operator skill or effort, is the dominant limiting factor. These findings show occlusion imposes a sensing limitation rather than a control or training deficiency, and that adapting camera viewpoint and sensing strategy to canopy density is needed to improve the system's accuracy and robustness.
作者：Daniel Udekwe, Hasan Seyyedhasani
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