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
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
9805489Informations 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.
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