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
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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Auteurs

Meng-Yang Lin (MY)

Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA.

Valerie Lynch (V)

Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA.

Dongdong Ma (D)

Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA.

Hideki Maki (H)

Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA.

Jian Jin (J)

Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA.

Mitchell Tuinstra (M)

Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA.

Classifications MeSH