High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance

Craig R. Yendrek, Tiago Tomaz, Christopher M. Montes, Youyuan Cao, Alison M. Morse, Patrick J Brown, Lauren M. McIntyre, Andrew Leakey, Elizabeth Ainsworth

Research output: Contribution to journalArticle

Abstract

High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500– 2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

Original languageEnglish (US)
Pages (from-to)614-626
Number of pages13
JournalPlant physiology
Volume173
Issue number1
DOIs
StatePublished - Jan 2017

Fingerprint

Chlorophyll
Least-Squares Analysis
reflectance
Zea mays
Nitrogen
phenotype
Phosphoenolpyruvate
corn
Photosynthesis
Sucrose
leaves
Gases
Genotype
nitrogen content
least squares
leaf area
chlorophyll
oxygen radical absorbance capacity
genetic variation
nondestructive methods

ASJC Scopus subject areas

  • Physiology
  • Genetics
  • Plant Science

Cite this

High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. / Yendrek, Craig R.; Tomaz, Tiago; Montes, Christopher M.; Cao, Youyuan; Morse, Alison M.; Brown, Patrick J; McIntyre, Lauren M.; Leakey, Andrew; Ainsworth, Elizabeth.

In: Plant physiology, Vol. 173, No. 1, 01.2017, p. 614-626.

Research output: Contribution to journalArticle

Yendrek, Craig R. ; Tomaz, Tiago ; Montes, Christopher M. ; Cao, Youyuan ; Morse, Alison M. ; Brown, Patrick J ; McIntyre, Lauren M. ; Leakey, Andrew ; Ainsworth, Elizabeth. / High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. In: Plant physiology. 2017 ; Vol. 173, No. 1. pp. 614-626.
@article{b66c0dc7886e43bb87652b50ae643886,
title = "High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance",
abstract = "High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500– 2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.",
author = "Yendrek, {Craig R.} and Tiago Tomaz and Montes, {Christopher M.} and Youyuan Cao and Morse, {Alison M.} and Brown, {Patrick J} and McIntyre, {Lauren M.} and Andrew Leakey and Elizabeth Ainsworth",
year = "2017",
month = "1",
doi = "10.1104/pp.16.01447",
language = "English (US)",
volume = "173",
pages = "614--626",
journal = "Plant Physiology",
issn = "0032-0889",
publisher = "American Society of Plant Biologists",
number = "1",

}

TY - JOUR

T1 - High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance

AU - Yendrek, Craig R.

AU - Tomaz, Tiago

AU - Montes, Christopher M.

AU - Cao, Youyuan

AU - Morse, Alison M.

AU - Brown, Patrick J

AU - McIntyre, Lauren M.

AU - Leakey, Andrew

AU - Ainsworth, Elizabeth

PY - 2017/1

Y1 - 2017/1

N2 - High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500– 2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

AB - High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500– 2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

UR - http://www.scopus.com/inward/record.url?scp=85008622688&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85008622688&partnerID=8YFLogxK

U2 - 10.1104/pp.16.01447

DO - 10.1104/pp.16.01447

M3 - Article

C2 - 28049858

AN - SCOPUS:85008622688

VL - 173

SP - 614

EP - 626

JO - Plant Physiology

JF - Plant Physiology

SN - 0032-0889

IS - 1

ER -