Classification of rice leaf blast severity using hyperspectral imaging

Abstract

AbstractRice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.