Evidence map›Paper›PMID 42733714›Full record

ArticlePlant phenomics (Washington, D.C.)2026

Accurate 3D maize ear phenotyping using voxel grids derived from RGB machine vision.

Leon H Oehme, Tobias Schrag, Peng Qi, Bastian Stürmer-Stephan, Khandoker T Ahammad, Daniel Wanke, Tobias Würschum, Xiongkui He, Joachim Müller

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In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

9 authors.

Leon H OehmeTropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, 70599, Germany.
Tobias SchragInstitute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, 70599, Germany.
Peng QiCollege of Science, China Agricultural University, Beijing, 100193, China.
Bastian Stürmer-StephanInstitute of Agricultural Engineering, University of Hohenheim, Stuttgart, 70599, Germany.
Khandoker T AhammadTropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, 70599, Germany.
Daniel WankeInstitute of Crop Science, Fertilisation and Soil Matter Dynamics, University of Hohenheim, Stuttgart, 70593, Germany.
Tobias WürschumInstitute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, 70599, Germany.
Xiongkui HeCollege of Science, China Agricultural University, Beijing, 100193, China.
Joachim MüllerTropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, 70599, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maize ear phenotyping is receiving increasing attention, driven by the demand of plant scientists for large-scale trait data and enabled by the rapid development of AI-based machine vision. While manual measurement of ear traits is labour-intensive and often destructive, existing digital solutions typically rely on a limited number of 2D images without capturing the entire 3D structure of an ear. Therefore, a novel 3D ear scanning pipeline was developed that computes voxel grids based on RGB images automatically captured from 100 different viewing angles. For kernel segmentation, open-source deep learning models for object detection and segmentation were evaluated and combined, achieving a dice score of 0.94 on 2D ear images. A novel strategy for mapping 2D segmentation masks to 3D-oriented voxels resulted in high coefficients of determination (

Indexed as

Deep learningMaizePhenotypingPhosphorusSegmentationThree-dimensionalVoxelization

Identifiers

PMID42733714
PMCPMC13571493

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Registered trials

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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.