Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling.
abiotic stress
corn
ecophysiology
high-throughput phenotyping
machine learning
nitrogen content
partial least square regression
relative water content
remote sensing
sorghum
Journal
Plants (Basel, Switzerland)
ISSN: 2223-7747
Titre abrégé: Plants (Basel)
Pays: Switzerland
ID NLM: 101596181
Informations de publication
Date de publication:
01 Mar 2022
01 Mar 2022
Historique:
received:
31
01
2022
revised:
15
02
2022
accepted:
28
02
2022
entrez:
10
3
2022
pubmed:
11
3
2022
medline:
11
3
2022
Statut:
epublish
Résumé
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (
Identifiants
pubmed: 35270145
pii: plants11050676
doi: 10.3390/plants11050676
pmc: PMC8912614
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : United Sorghum Checkoff
ID : Grant Number 41000314
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