A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging

Ali Moghimi, Ce Yang, Marisa E. Miller, Shahryar F. Kianian, Peter M. Marchetto

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesianmethod. The rankings of bothmethods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

Original languageEnglish (US)
Article number1182
JournalAsian Journal of Pharmaceutical and Clinical Research
Volume9
DOIs
StatePublished - Jan 1 2018

Fingerprint

Salt-Tolerance
Triticum
Salts
Hydroponics
Salinity
Research

Keywords

  • Bayesian inference
  • Histogram distance
  • Hyperspectral imaging
  • Image processing
  • Machine learning
  • Plant phenotyping
  • Salt stress
  • Wheat

Cite this

@article{dc9faee30d3d46609ef572b23f7e7826,
title = "A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging",
abstract = "Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesianmethod. The rankings of bothmethods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.",
keywords = "Bayesian inference, Histogram distance, Hyperspectral imaging, Image processing, Machine learning, Plant phenotyping, Salt stress, Wheat",
author = "Ali Moghimi and Ce Yang and Miller, {Marisa E.} and Kianian, {Shahryar F.} and Marchetto, {Peter M.}",
year = "2018",
month = "1",
day = "1",
doi = "10.3389/fpls.2018.01182",
language = "English (US)",
volume = "9",
journal = "Asian Journal of Pharmaceutical and Clinical Research",
issn = "0974-2441",
publisher = "Asian Journal of Pharmaceutical and Clinical Research",

}

TY - JOUR

T1 - A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging

AU - Moghimi, Ali

AU - Yang, Ce

AU - Miller, Marisa E.

AU - Kianian, Shahryar F.

AU - Marchetto, Peter M.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesianmethod. The rankings of bothmethods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

AB - Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesianmethod. The rankings of bothmethods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

KW - Bayesian inference

KW - Histogram distance

KW - Hyperspectral imaging

KW - Image processing

KW - Machine learning

KW - Plant phenotyping

KW - Salt stress

KW - Wheat

UR - http://www.scopus.com/inward/record.url?scp=85053076272&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053076272&partnerID=8YFLogxK

U2 - 10.3389/fpls.2018.01182

DO - 10.3389/fpls.2018.01182

M3 - Article

C2 - 30197650

AN - SCOPUS:85053076272

VL - 9

JO - Asian Journal of Pharmaceutical and Clinical Research

JF - Asian Journal of Pharmaceutical and Clinical Research

SN - 0974-2441

M1 - 1182

ER -