Abstract
A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions are needed to allow automated trait estimation at scale, but training data is scarce and human annotations are often inaccessible because they reside in proprietary software that only exports per-image scalar totals of root length and surface area. Nevertheless, large numeric archives of these root traits already exist. RootQuant showed that the traits can be predicted directly from the whole image by regression, thus removing manually traced masks from the pipeline; RootQuantV2 takes that idea further by replacing RootQuant's CNN backbone with a self-supervised ViT. We adapt a frozen DINOv3 ViT-L/16 with a hybrid parameter-efficient scheme. Training only 11.9M parameters (3.78% of the model), RootQuantV2 achieves length and area $R^2$ of 0.950 and 0.930, respectively, while lowering length/area RMSE by 24.3%/20.7% over RootQuant. RootQuantV2 thus repurposes legacy numeric archives for high-throughput, automated root trait estimation.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Parth, K., Varela, S., & Leakey, A. D. B. (2026). RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery. https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery
MLA 9
Parth, Kinjalk, et al. "RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery." https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery.
Chicago (author–date)
Parth, Kinjalk, Sebastian Varela, and Andrew D. B. Leakey. 2026. "RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery." https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery.
Harvard
Parth, K., Varela, S. and Leakey, A. D. B. (2026) 'RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery', Available at: https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery.
Vancouver
Parth K, Varela S, Leakey ADB. RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery. https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery
IEEE
K. Parth, S. Varela, and A. D. B. Leakey, "RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery," https://omanscience.com/en/articles/rootquantv2-adapting-a-vision-foundation-model-for-root-trait-regression-from-minirhizotron-imagery.