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 language||English (US)|
|Title of host publication||Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II|
|Editors||Robert J. Moorhead, J. Alex Thomasson, Mac McKee|
|State||Published - 2017|
|Event||Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017 - Anaheim, United States|
Duration: Apr 10 2017 → Apr 11 2017
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Other||Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II 2017|
|Period||4/10/17 → 4/11/17|
Bibliographical notePublisher Copyright:
© 2017 SPIE.
- Hyperspectral Imaging
- Salt Stress