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Detection of crop stress from hyperspectral images is of high importance for breeding and precision crop protection. However, thecontinuous monitoring of stress in phenotyping facilities by hyperspectral imagers produces huge amounts of uninterpreted data. Inorder to derive a stress description from the images, interpreting algorithms with high prediction performance are required. Based ona static model, the local stress state of each pixel has to be predicted. Due to the low computational complexity, linear models arepreferable.  In this paper, we focus on drought-induced stress which is represented by discrete stages of ordinal order. We present and compare fivemethods which are able to derive stress levels from hyperspectral images: One-vs.-one Support Vector Machine (SVM), one-vs.-allSVM, Support Vector Regression (SVR), Support Vector Ordinal Regression (SVORIM) and Linear Ordinal SVM classification. Themethods are applied on two data sets - a real world set of drought stress in single barley plants and a simulated data set. It is shown,that Linear Ordinal SVM is a powerful tool for applications which require high prediction performance under limited resources. It issignificantly more efficient than the one-vs.-one SVM and even more efficient than the less accurate one-vs.-all SVM. Compared to thevery compact SVORIM model, it represents the senescence process much more accurate

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Ordinal classification for efficient plant stress prediction in hyperspectral data