Derivation of stress severities in wheat from hyperspectral data using support vector regression

The benefits and limitations of crop stress detection by hyperspectral data analysis have been examined in detail. It could thereby be demonstrated that even a differentiation between healthy and fungal infected wheat stands is possible and profits by analyzing entire spectra or specifically selected spectral bands/ranges. For reasons of practicability in agriculture, spatial information about the health status of crop plants beyond a binary classification would be a major benefit. Thus, the potential of hyperspectral data for the derivation of several disease severity classes or moreover the derivation of continual disease severity has to be further examined. In the present study, a state-of-the-art regression approach using support vector machines (SVM) has been applied to hyperspectral AISA-Dual data to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat. Ground truth disease ratings were realized within an experimental field. A mean correlation coefficient of r=0.69 between severities and support vector regression predicted severities could be achieved using indepent training and test data. The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression.