Location: Cereal Disease LabTitle: A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging
|MOGHIMI, ALI - University Of Minnesota|
|YANG, CE - University Of Minnesota|
|MILLER, MARISA - University Of Minnesota|
|MARCHETTO, PETER - University Of Minnesota|
Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2018
Publication Date: 8/24/2018
Citation: Moghimi, A., Yang, C., Miller, M.E., Kianian, S., Marchetto, P.M. 2018. A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Frontiers in Plant Science. 9(1182). https://doi.org/10.3389/fpls.2018.01182.
Interpretive Summary: Salinity stress is a major abiotic stress that affects the growth and development of plants and thus limits crop production and yield. Large amount of crop land is lost each year due to increased salt content. 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. Hyperspectral images were captured one day after salt application when there were no visual symptoms. Detection of tolerant wheat lines was achieved as early as one day after the salt treatment when no visual symptoms were observed, and physiological and growth measurements were not yet possible. Early detection enables faster screening cycles and reduces the energy and costs needed to maintain plants in a controlled environment. The findings of this experiment provide evidence that breeders and plant geneticists would be able to properly manage time, energy, cost, and space in greenhouses while maintain accuracy and improve precision by implementing hyperspectral imaging and the proposed analytical methods. Faster assessment of stress tolerance is a major advantage to breeding programs and basic research alike.
Technical 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 200 mM 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 measure. 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 Bayesian method. The rankings of both methods 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.