Virtual reality boosts accuracy in 3D root system reconstruction

In controlled laboratory tests using synthetic root datasets, participants employing VRoot achieved significantly higher F1 scores, particularly under noisy conditions, and reported improved user experience.

Plant root systems play a critical role in water uptake, nutrient acquisition, and stress resilience. Understanding plant root system architecture (RSA) is essential for advancing sustainable agriculture, yet extracting accurate root data from 3D imaging remains challenging—especially when automatic methods struggle with noisy or complex datasets. Advances in non-invasive 3D imaging—such as magnetic resonance imaging (MRI)—allow researchers to visualize roots in soil without disrupting growth, but converting image data into usable digital models remains a major bottleneck. Automatic extraction tools often misinterpret root structures due to imaging noise, soil artifacts, or incomplete visibility. Manual corrections by experts can improve results, but conventional desktop interfaces limit spatial perception and interaction efficiency. Based on these challenges, there is a need for more intuitive, accurate, and adaptable tools for 3D root reconstruction.

A study (DOI: 10.1016/j.plaphe.2025.100013) published in Plant Phenomics on 26 March 2025 by Dirk N. Baker’s team, Forschungszentrum Jülich GmbH, demonstrates VR’s potential to bridge gaps in root phenotyping, opening new possibilities for plant science and crop improvement.

The research team designed VRoot, a VR application for immersive RSA reconstruction, enabling users to interact directly with 3D volumetric root data. Wearing a head-mounted display and using tracked controllers, participants navigated MRI-derived soil column models, tracing root paths and making real-time adjustments in a fully three-dimensional workspace. The study compared VRoot against NMRooting, a leading desktop-based root extraction tool, using synthetic datasets with and without water-induced noise. Untrained participants completed root tracing tasks under both conditions. Results showed that VRoot consistently delivered higher F1 scores—indicating better precision and recall—than NMRooting, with the performance gap widening when water noise was present. VR users also achieved more accurate measurements of root length and inter-lateral distance, and their reconstructions more closely matched ground truth models. Usability assessments, based on the System Usability Scale and pragmatic quality ratings, further favored VRoot, especially for noisy datasets. While some participants initially experienced depth-perception or navigation challenges, brief training sessions mitigated these issues. The findings underscore VR’s ability to enhance spatial awareness, reduce tracing errors, and improve user satisfaction in root phenotyping tasks.

By enabling more accurate manual reconstructions in challenging datasets, VRoot expands the scope of root phenotyping to include diverse soil types and moisture conditions that confound automated algorithms. This capability is valuable for functional-structural plant modeling, where precise RSA data inform simulations of water and nutrient uptake. The technology could benefit plant breeders, crop scientists, and soil ecologists seeking to link root traits with performance under field-relevant stresses.

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References

DOI

10.1016/j.plaphe.2025.100013

Original Source URL

https://doi.org/10.1016/j.plaphe.2025.100013

Funding information

The authors would like to acknowledge funding provided by the German government to the Gauss Centre for Supercomputing via the InHPC-DE project (01—H17001).

This work has partly been funded by the EUROCC2 project funded by the European High- Performance Computing Joint Undertaking (JU) and EU/EEA states under grant agreement No 101101903.

This work has partly been funded by the German Research Foundation under Germany's Excel-lence Strategy, EXC-2070 - 390732324 - PhenoRob and by the German Federal Ministry of Education and Research (BMBF) in the framework of the funding initiative Soil as a Sustainable Resource for the Bioeconomy BonaRes, the project BonaRes (Module A): Sustainable Subsoil Management - Soil3; subproject 3 (grant 031B1066C).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.