基于虚拟现实的温室园艺远程人机交互
Remote Human-Robot Interaction In Greenhouses via Virtual Reality: How Plant Canopy Structure Affects Leaf Disease and Soil Moisture Inspection
- School of Plant and Environmental Science, Virginia Tech（弗吉尼亚理工大学植物与环境科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究验证了VR远程人机交互用于温室叶片检查和土壤湿度评估的有效性，发现摄像头遮挡是主要限制因素，需调整传感策略提升系统性能。
AI中文摘要：
本研究评估了在温室环境中采用虚拟现实（VR）技术进行远程人机交互以完成叶片检查和土壤湿度评估的有效性。该机器人系统由一辆无人地面车辆和一个配备摄像头的机械臂组成，其导航和机械臂控制由运动学模型调控。两项实验共检查了14株不同的植物，实验采用VR遥操作模式，研究过程遵循一组预先设定的研究问题和假设。在叶片检查实验中，循环完成时间介于3.3秒至8.0秒之间，基于植物的病害检测准确率最高达到88%；第二实验中病斑检测数值上有所提升，但该变化无统计学意义（p=0.378）。在土壤湿度评估方面，实验成功判定14株植物中9株（占比64.3%）的浇水需求，其中植物1、2、3、8、9、10和13的判定结果始终一致；但该提升同样无统计学意义（p=0.50）。事后分析显示，土壤湿度评估的可靠性受植物冠层形态的显著强预测（p<0.01）：第二实验中，具有宽阔单叶冠层的植物判定成功率达100%，而具有密集复叶冠层的植物仅为16.7%。次要分析表明，操作人员尝试处理密集冠层植物的速度明显加快，但成功率未随之提升，说明摄像头遮挡而非操作人员技能或努力是主要限制因素。这些结果表明，遮挡带来的是传感限制而非控制或训练不足，需要根据冠层密度调整摄像头视角和传感策略以提升系统的准确性和鲁棒性。
英文摘要：
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.
