Comparative Evaluation of VR-Enabled Robots and Human Operators for Targeted Disease Management in Vineyards

2025-07-05

AI Summary

This study addresses the dual challenges of low human operational efficiency and insufficient robot autonomy in vineyard-targeted disease management. We propose and evaluate an immersive virtual reality (VR)-enabled robotic teleoperation framework. Methodologically, we develop a Unity-ROS co-simulation platform integrating immersive and non-immersive VR interfaces, disease-data memory storage, playback functionality, and yield-map-driven path-planning navigation. Results demonstrate that VR-augmented robotic operation achieves a 65% speedup over human operators in repetitive treatment tasks and a 38% improvement in map-guided navigation; although manual scanning is marginally slower, the integrated “memory + VR + path planning” paradigm proves effective for precision agriculture. Our key contribution is the first integration of immersive VR with data-memory-driven automated planning, significantly enhancing both operational efficiency and deployability of agricultural robots in structured environments.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

This study explores the use of immersive virtual reality (VR) as a control interface for agricultural robots in vineyard disease detection and treatment. Using a Unity-ROS simulation, it compares three agents: a human operator, an immersive VR-controlled robot, and a non-immersive VR-controlled robot. During the scanning phase, humans perform best due to agility and control speed. However, in the treatment phase, immersive VR robots outperform others, completing tasks up to 65% faster by using stored infection data and optimized path planning. In yield-map-based navigation, immersive robots are also 38% faster than humans. Despite slower performance in manual scanning tasks, immersive VR excels in memory-guided, repetitive operations. The study highlights the role of interface design and path optimization, noting limitations in simulation fidelity and generalizability. It concludes that immersive VR has strong potential to enhance efficiency and precision in precision agriculture.

Problem

• Comparing VR robots and humans for vineyard disease management
• Evaluating efficiency of VR interfaces in agricultural tasks
• Assessing path optimization for precision agriculture using VR

Innovation

• VR-controlled robots for vineyard disease management
• Unity-ROS simulation compares human and robot agents
• Immersive VR robots optimize path planning and treatment