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Proceedings Paper

Hyperspectral imaging to identify salt-tolerant wheat lines
Author(s): Ali Moghimi; Ce Yang; Marisa E. Miller; Shahryar Kianian; Peter Marchetto
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Paper 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.

Paper Details

Date Published: 16 May 2017
PDF: 7 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 1021805 (16 May 2017); doi: 10.1117/12.2262388
Show Author Affiliations
Ali Moghimi, Univ. of Minnesota, Twin Cities (United States)
Ce Yang, Univ. of Minnesota, Twin Cities (United States)
Marisa E. Miller, Agricultural Research Service (United States)
Shahryar Kianian, Agricultural Research Service (United States)
Peter Marchetto, Univ. of Minnesota, Twin Cities (United States)

Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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