Enhancing Plant Variety Discovery Process with Visual Trait Assessment in VR

Plant breeders use field trials across locations and years to identify superior plant varieties with traits such as disease resistance and higher yield. However, comparing breeding candidates across locations and years is challenging and resource demanding. To address this, we developed an integrated system that combines data acquisition through a field robot with an immersive virtual reality (VR) interface for remote assessments. The robot autonomously collects images, spectral data and 3D scans of canola breeding trials. Our VR application, developed through a user-centered approach, offers photo-realistic 3D visualizations, enabling breeders to compare candidates across locations and growth stages—capabilities unavailable in field assessments. In a user study, five breeders conducted visual trait scoring in VR to evaluate how well the system supported typical field trial tasks. The results demonstrated consistent scoring patterns among raters. Feedback from breeders indicated that the ability to compare candidates across locations and growth stages enhanced their decision making in trait assessment. This work highlights the potential of combining robotics and VR to transform data-intensive processes in agriculture.