Evidence map›Paper›PMID 38217351›Full record

ReviewPlant biotechnology journal2024

Plant microphenotype: from innovative imaging to computational analysis.

Ying Zhang, Shenghao Gu, Jianjun Du, Guanmin Huang, Jiawei Shi, Xianju Lu, Jinglu Wang, Wanneng Yang, Xinyu Guo, Chunjiang Zhao

Abstract readReview
In one paragraph

Review in Plant biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

10 authors.

Ying Zhang *Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.ORCID 0000-0001-5275-0813
Shenghao Gu *Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Jianjun Du *Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Guanmin HuangBeijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Jiawei ShiNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
Xianju LuBeijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Jinglu WangBeijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Wanneng YangNational Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.ORCID 0000-0003-1095-1355
Xinyu GuoBeijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Chunjiang ZhaoBeijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Funding

Beijing Academy of Agriculture and Forestry Sciences Grants KJCX20220401Beijing Academy of Agriculture and Forestry Sciences Grants KJCX20230429Beijing Academy of Agriculture and Forestry Sciences Grants QNJJ202124China Agriculture Research System CARS-02China Agriculture Research System CARS-54Development Project of Beijing Academy of Agriculture and Forestry Sciences, Young Elite Scientist Sponsorship Program by BAST BYESS2023204National Key Research and Development Program 2022YFD2002300the Construction of Collaborative Innovation Centre of Beijing Academy and Forestry Science KJCX201917the National Nature Science Foundation of China 32001420the National Nature Science Foundation of China U21A20205
6 · The paper itself

Abstract

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Indexed as

Artificial IntelligenceGenomicsGenotypePhenotypePlantscomputational phenotypinggenetic effectsimaging techniquemicrophenotypetrait identification

Identifiers

PMID38217351
PMCPMC10955502

What OpenQuestion holds

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LicenceCC BY
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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.