Multimodal Mixed Reality for In‐Field Visualization of RGB‐Based Vegetation Indices

Evaluating crop physiological health in field environments remains constrained by offline data processing or static 2D displays, limiting real‐time spatial interpretation. This paper presents a novel mixed reality framework that calculates, normalizes, and visualizes 20 RGB‐based vegetation indices directly in agricultural fields using consumer‐grade head‐mounted displays. By processing live camera streams through parallel GPU compute shaders and dynamic Look‐Up Table color maps, the platform provides contextualized feedback across four task‐tailored interaction modalities: Immersive, Screen, Scan, and Mosaic. Quantitative in‐field benchmarking on a Meta Quest 3 headset demonstrates that GPU parallelization eliminates severe CPU processing bottlenecks, maintaining a motion‐stable refresh rate (≥72 Hz) with zero stale frames to prevent cybersickness during field traversal. To evaluate interface ergonomics without empirical human subject trials, a theoretical usability assessment grounded in Nielsen’s 10 Usability Heuristics and the cognitive dimensions of notations framework was conducted. The evaluation confirms that the interaction design effectively balances high spectral information density with low cognitive workload and unoccluded peripheral hazard visibility. This work establishes a scalable foundation for real‐time spatial computing in precision agriculture, enabling cost‐effective, in situ crop analytics.