用虚拟现实远程操控机器人巡检温室，发现遮挡是主要感知瓶颈。
Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality
- 通过虚拟现实遥控机械臂与无人车完成植物巡检
- 叶片病害识别最高达88%，土壤湿度判断成功64.3%
- 密集叶冠植物检测失败率高，需调整视角和感知策略
本研究评估了在温室环境中，利用虚拟现实进行远程人机交互对叶片检查与土壤湿度评估的有效性。机器人系统由无人地面车辆和带摄像头的机械臂组成，基于运动学模型实现导航与操作控制。通过两轮实验，对14株植物进行了检查，依据预设的研究问题与假设开展分析。叶片检查中，单周期完成时间在3.3至8.0秒之间，病害识别准确率最高达88%；第二次实验中病斑检测有数值提升，但未达统计显著（p=0.378）。土壤湿度评估在14株植物中成功判断浇水需求共9株（64.3%），其中1、2、3、8、9、10、13号植物表现稳定。该改进亦无统计意义（p=0.50）。事后分析显示，土壤湿度评估可靠性受植株冠层形态显著影响（p<0.01）：具宽大单叶冠层的植物在第二轮实验中成功率达100%，而密集复叶冠层仅16.7%。次级分析表明操作员虽对密集冠层植物尝试速度加快，但成功率未提高，说明相机遮挡而非操作技能或努力是主要限制因素。结果表明，遮挡导致的是感知局限，非控制或训练缺陷，需根据冠层密度调整摄像机视角与感知策略以提升系统准确性与鲁棒性。
原文摘要 · Abstract (English)
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.
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