Hyperspectral imaging to identify salt-tolerant wheat lines

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In order to address the worldwide growing demand for food, agriculture is facing certain challenges and limitations. One of the important threats limiting crop productivity is salinity. Identifying salt tolerate varieties is crucial to mitigate the negative effects of this abiotic stress in agricultural production systems. Traditional measurement methods of this stress, such as biomass retention, are labor intensive, environmentally influenced, and often poorly correlated to salinity stress alone. In this study, hyperspectral imaging, as a non-destructive and rapid method, was utilized to expedite the process of identifying relatively the most salt tolerant line among four wheat lines including Triticum aestivum var. Kharchia, T. aestivum var. Chinese Spring, (Ae. columnaris) T. aestivum var. Chinese Spring, and (Ae. speltoides) T. aestivum var. Chinese Spring. To examine the possibility of early detection of a salt tolerant line, image acquisition was started one day after stress induction and continued on three, seven, and 12 days after adding salt. Simplex volume maximization (SiVM) method was deployed to detect superior wheat lines in response to salt stress. The results of analyzing images taken as soon as one day after salt induction revealed that Kharchia and (columnaris)Chinese Spring are the most tolerant wheat lines, while (speltoides) Chinese Spring was a moderately susceptible, and Chinese Spring was a relatively susceptible line to salt stress. These results were confirmed with the measuring biomass performed several weeks later.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
EditorsRobert J. Moorhead, J. Alex Thomasson, Mac McKee
PublisherSPIE
Volume10218
ISBN (Electronic)9781510609372
DOIs
StatePublished - Jan 1 2017
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017 - Anaheim, United States
Duration: Apr 10 2017Apr 11 2017

Other

OtherAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017
CountryUnited States
CityAnaheim
Period4/10/174/11/17

Fingerprint

Hyperspectral Imaging
wheat
Wheat
Triticum Aestivum
Salt
Salts
salts
Line
Salt Stress
Salinity
Biomass
biomass
salinity
Proof by induction
induction
Image Acquisition
Agriculture
Production Systems
agriculture
labor

Keywords

  • Hyperspectral Imaging
  • Phenotyping
  • Salt Stress
  • SiVM
  • Wheat

Cite this

Moghimi, A., Yang, C., Miller, M. E., Kianian, S., & Marchetto, P. (2017). Hyperspectral imaging to identify salt-tolerant wheat lines. In R. J. Moorhead, J. A. Thomasson, & M. McKee (Eds.), Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II (Vol. 10218). [1021805] SPIE. https://doi.org/10.1117/12.2262388

Hyperspectral imaging to identify salt-tolerant wheat lines. / Moghimi, Ali; Yang, Ce; Miller, Marisa E.; Kianian, Shahryar; Marchetto, Peter.

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. ed. / Robert J. Moorhead; J. Alex Thomasson; Mac McKee. Vol. 10218 SPIE, 2017. 1021805.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Moghimi, A, Yang, C, Miller, ME, Kianian, S & Marchetto, P 2017, Hyperspectral imaging to identify salt-tolerant wheat lines. in RJ Moorhead, JA Thomasson & M McKee (eds), Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. vol. 10218, 1021805, SPIE, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017, Anaheim, United States, 4/10/17. https://doi.org/10.1117/12.2262388
Moghimi A, Yang C, Miller ME, Kianian S, Marchetto P. Hyperspectral imaging to identify salt-tolerant wheat lines. In Moorhead RJ, Thomasson JA, McKee M, editors, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. Vol. 10218. SPIE. 2017. 1021805 https://doi.org/10.1117/12.2262388
Moghimi, Ali ; Yang, Ce ; Miller, Marisa E. ; Kianian, Shahryar ; Marchetto, Peter. / Hyperspectral imaging to identify salt-tolerant wheat lines. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. editor / Robert J. Moorhead ; J. Alex Thomasson ; Mac McKee. Vol. 10218 SPIE, 2017.
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