Grading method of rice panicle blast severity based on hyperspectral image

Estimation of panicle blast level plays an important role in high-quality production of rice. It helps to quantitatively assess the level of blast resistance and severity in the field to make appropriate decisions in gauging cultivar resistance in rice breeding or precisely controlling blast epidemic. However, it is difficult to evaluate the blast disease degree automatically and accurately. In this study, a novel grading method for panicle blast severity based on hyperspectral imaging technology is proposed. The method defines a bag of spectrum words (BoSW) model for hyperspectral image data representation. The BoSW model based on hyperspectral image data representation is used as the input of a Chi-square kernel support vector machine (Chi-SVM) classifier for predicting the rice panicle blast level. This research improves the classification accuracy of rice panicle blast grading and provides a reference to evaluate other disease level grading as well.