Evidence map›Paper›PMID 42178486›Full record

ReviewJournal of experimental botany2026

Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.

Geunmuk Im, JeongHo Baek, Jinsu Kim, Song Lim Kim

Abstract readReview
In one paragraph

Review in Journal of experimental botany, 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

4 authors.

Geunmuk ImDigital Breeding Convergence Division, National Institute of Agricultural Sciences, RDA, Jeonju 54874, Republic of Korea.ORCID 0009-0009-9607-7435
JeongHo BaekDigital Breeding Convergence Division, National Institute of Agricultural Sciences, RDA, Jeonju 54874, Republic of Korea.ORCID 0009-0007-5961-0697
Jinsu KimDigital Breeding Convergence Division, National Institute of Agricultural Sciences, RDA, Jeonju 54874, Republic of Korea.ORCID 0009-0002-9863-963X
Song Lim KimDigital Breeding Convergence Division, National Institute of Agricultural Sciences, RDA, Jeonju 54874, Republic of Korea.ORCID 0000-0001-5573-101X

Funding

National Institute of Agricultural SciencesRDA Fellowship Program PJ01757101Rural Development Administration, Republic of Korea
6 · The paper itself

Abstract

Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.

Indexed as

Crops, AgriculturalPlant RootsPhenomicsPhenotypeArtificial intelligencehigh-throughput phenotypingnon-invasive imagingplant phenomicsroot phenotypingsustainable agriculture

Identifiers

PMID42178486
PMCPMC13529341

What OpenQuestion holds

Textmetadata
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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.