Classification Of Rice Leaf Blast Severity Using Hyperspectral Imaging

Abstract Rice leaf blast is prevalent all over the world and is a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology applied in plant disease research. In this paper, the standard deviation (STD) of the spectral reflectance of whole leaves was calculated and a support vector machine (SVM) model was built to classify the degree of rice leaf blast at different growth stages. The average accuracy of the full-spectrum-based SVM model at jointing stage, booting stage and heading stage was 97.78%, 92.63% and 92.20%, respectively. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also those with the same disease level. Compared with the raw spectral reflectance, the STD of which performed better in assessing rice leaf blast severity.