Evidence map›Paper›PMID 37309480›Full record

ArticleMolecular breeding : new strategies in plant improvement2021

A high-throughput and low-cost maize ear traits scorer.

Xiuying Liang, Junli Ye, Xiaoyu Li, Zhixin Tang, Xuehai Zhang, Wenqiang Li, Jianbing Yan, Wanneng Yang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular breeding : new strategies in plant improvement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

4 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
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

8 authors at 2 institutions in 1 country.

Xiuying Liang *National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Junli Ye *National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Xiaoyu LiNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Zhixin TangNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Xuehai ZhangHenan Agricultural University, Xuchang, 461101 People's Republic of China.
Wenqiang LiNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Jianbing YanNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Wanneng YangNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics, College of Engineering, Crop Information Center, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.ORCID 0000-0003-1095-1355
Huazhong Agricultural University · CNHenan Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, based on automatic control and image processing, a high-throughput and low-cost maize ear traits scorer (METS) was developed for the automatic measurement of 34 maize ear traits. In total, 813 maize ears were measured using METS, and the results showed that the square of the correlation coefficient ( Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01205-4.

Indexed as

Automatic measurementHigh-throughput methodImage processingMaize ear traitsSupport vector machine (SVM)

Identifiers

PMID37309480
PMCPMC10236123
OpenAlexW3133334185

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.