ABSTRACT
Wheat stripe rust, caused by Puccinia strii formis f. sp. tritici, is one of pandemic diseases causing severe losses in China. Monitoring and warning of this disease is principal for its precise prediction and for implementing effective measures to control it. The hyperspectral data used for analysis were attained from 88 leaves including healthy leaves and infected leaves over a range of disease severity levels. Support vector machine (SVM) was applied to classify and identify the severity of wheat leaves infected by the pathogen. The model was built based on 44 proof-read samples to estimate 44 proof-test samples. And the identification accuracy is totally 97%. So SVM can be used in the classification and identification of severity of wheat stripe rust based on attained hyperspectral data.
