KAT4IA:


Journal

Plant phenomics (Washington, D.C.)
ISSN: 2643-6515
Titre abrégé: Plant Phenomics
Pays: United States
ID NLM: 101769942

Informations de publication

Date de publication:
2021
Historique:
received: 11 01 2021
accepted: 05 07 2021
entrez: 18 8 2021
pubmed: 19 8 2021
medline: 19 8 2021
Statut: epublish

Résumé

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses

Identifiants

pubmed: 34405144
doi: 10.34133/2021/9805489
pmc: PMC8358166
doi:

Types de publication

Journal Article

Langues

eng

Pagination

9805489

Informations de copyright

Copyright © 2021 Xingche Guo et al.

Déclaration de conflit d'intérêts

The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work.

Références

Trends Plant Sci. 2014 Jan;19(1):52-61
pubmed: 24139902
Plant Methods. 2018 May 10;14:35
pubmed: 29760766
Front Plant Sci. 2016 Sep 22;7:1419
pubmed: 27713752
Plant Methods. 2017 Nov 1;13:79
pubmed: 29118821
Funct Plant Biol. 2016 Feb;44(1):143-153
pubmed: 32480553
BMC Bioinformatics. 2011 May 12;12:148
pubmed: 21569390
Curr Opin Plant Biol. 2015 Apr;24:93-9
pubmed: 25733069
PeerJ. 2017 Dec 1;5:e4088
pubmed: 29209576
Gigascience. 2019 Mar 1;8(3):
pubmed: 30715329
Plant Physiol. 2016 Oct;172(2):823-834
pubmed: 27528244
Plant Methods. 2018 Aug 4;14:66
pubmed: 30087695
Rice (N Y). 2014 Dec;7(1):16
pubmed: 26055997
Plant Physiol. 2014 Apr 23;165(2):506-518
pubmed: 24760818
Front Plant Sci. 2017 Jul 07;8:1190
pubmed: 28736569
Plant Phenomics. 2020 Jul 14;2020:7481687
pubmed: 33313562
Sensors (Basel). 2014 Oct 24;14(11):20078-111
pubmed: 25347588
Plant Methods. 2017 Jan 31;13:7
pubmed: 28163771
Plant Methods. 2017 Dec 22;13:117
pubmed: 29299051
Plant Phenomics. 2019 Jun 27;2019:1525874
pubmed: 33313521

Auteurs

Xingche Guo (X)

Department of Statistics, Iowa State University, Iowa, USA.

Yumou Qiu (Y)

Department of Statistics, Iowa State University, Iowa, USA.

Dan Nettleton (D)

Department of Statistics, Iowa State University, Iowa, USA.

Cheng-Ting Yeh (CT)

Plant Sciences Institute, Iowa State University, Iowa, USA.
Department of Agronomy, Iowa State University, Iowa, USA.

Zihao Zheng (Z)

Department of Agronomy, Iowa State University, Iowa, USA.

Stefan Hey (S)

Department of Agronomy, Iowa State University, Iowa, USA.

Patrick S Schnable (PS)

Plant Sciences Institute, Iowa State University, Iowa, USA.
Department of Agronomy, Iowa State University, Iowa, USA.

Classifications MeSH