The dataset contains hyperspectral images as well as data obtained by conventional phenotyping experiments for four wheat lines. The primary goal of this study was to identify wheat lines tolerant to salt stress since salinity stress has significant adverse effects on crop productivity and yield. We leveraged the high spectral resolution of hyperspectral images and utillized machine learning algorithms to overcome the complexity and high-dimensionality of images. Hyperspectral images were captured one day after salt application when there were no visual symptoms in wheat plants. We could assess the salt stress in wheat lines in a quantitative, interpretable, and non-destructive manner while reducing cost, time, and labor input.
|Date made available||2018|
|Publisher||Data Repository for the University of Minnesota|