Evidence map›Paper›PMID 41656271›Full record

ArticlePlant methods2026

OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.

Shaoqi Fan, Guoji Li, Revocatus Bahitwa, Zhiguo Jia, Hongwei Zhang, Jinghong Shao, Qiuying Yu, Xiaoran Chen, Yiheng Qian, Mingchi Xu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant methods, 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

12 authors.

Shaoqi FanState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Guoji LiState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Revocatus BahitwaState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Zhiguo JiaState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Hongwei ZhangState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Jinghong ShaoState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Qiuying YuState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Xiaoran ChenState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Yiheng QianState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Mingchi XuState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Linlin ZhuState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Hai WangState Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Center for Crop Functional Genomics and Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China. wanghai@cau.edu.cn.ORCID http://orcid.org/0000-0002-1722-1518

Funding

National Key Research and Development Program of China 2022YFD1201100Special Guidance Fund for Central Universities 2025AC030
6 · The paper itself

Abstract

Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length (R

Indexed as

Computer visionDeep learningDIYPhenotypingZea mays

Identifiers

PMID41656271
PMCPMC12977455

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.