RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery.

Kinjalk Parth, Sebastian Varela, Andrew D B Leakey

Abstract readPreprint

In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kinjalk ParthDepartment of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Sebastian VarelaCenter for Advanced Bioenergy and Bioproducts Innovation, Urbana, IL 61801, USA.

Andrew D B LeakeyDepartment of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

Minirhizotron root phenotypingParameter-efficient fine-tuningSegmentation-free trait regressionSelf-supervised ViT adaptationVision foundation models

Identifiers

PMID42818480

PMCPMC13622434

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.