Evidence map›Paper›PMID 38732392›Full record

ArticlePlants (Basel, Switzerland)2024

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.

Hang Liu, Hongfei Zhu, Fei Liu, Limiao Deng, Guangxia Wu, Zhongzhi Han, Longgang Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Hang LiuCollege of Grassland Science, Qingdao Agricultural University, Qingdao 266109, China.
Hongfei ZhuCollege of Computer Science and Technology, Tiangong University, Tianjin 300387, China.
Fei LiuCollege of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.
Limiao DengCollege of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.
Guangxia WuCollege of Agronomy, Qingdao Agricultural University, Qingdao 266109, China.ORCID 0000-0002-1009-8975
Zhongzhi HanCollege of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.ORCID 0000-0002-5662-319X
Longgang ZhaoCollege of Grassland Science, Qingdao Agricultural University, Qingdao 266109, China.

Funding

Central Guide local development Special - Science and Technology Commissioners Action Plan Project 22134-zyyd-nsh, 23139-zyyd-nshNational Key Research and Development Program 2022YFD2300101-1Shandong Major Science and Technology Innovation Project 2021TZXD003-003, 2021LZGC026-05Shandong Province Agricultural Seed Improvement Project 2021LZGC003Shandong Province Key Research and Development Program 2021LZGC026-03Shandong Provincial Science and Technology smes Promotion Project 2021TSGC1016, 2022TSGC1114Shandong Taishan Scholars Project Project 2021-216Shandong University Youth Innovation Team 2020KJF004Yellow Triangle National Agricultural High Zone Science and Technology Special Project 2022SZX18
6 · The paper itself

Abstract

The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify

Indexed as

Arabidopsis thalianabreedingdeep learningorganelleplant phenotypes

Identifiers

PMID38732392
PMCPMC11085357

What OpenQuestion holds

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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.