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Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality

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arXiv:2608.27545v1 Announce Type: new 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…

SourcearXiv RoboticsAuthor: Daniel Udekwe, Hasan Seyyedhasani
Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality
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[Submitted on 27 Aug 2026]

Title:Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality

View a PDF of the paper titled Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality, by Daniel Udekwe and 1 other authors

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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

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  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2608.27545v1 Announce Type: new Abstract: This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moistur…

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