Early Identification of Rice Leaf Blast Based on Hyperspectral Imaging

Abstract

Rice blast is one of the three major rice diseases recognized in the world, which greatly harms the quality and the yield of rice. In order to distinguish rice leaf blast disease from nutrient deficiency and diagnose early the leaf blast disease, this study was based on the natural incidence of rice and field experiments, hyperspectral imagers were used to obtain the imaging spectrum of health, nitrogen deficiency, mild disease and severe disease. Spectra of 4 types of leaves were extracted, and three kinds of different data pretreatment methods were used, and the SPA feature extraction method was combined with the support vector machine(SVM) and the linear discriminant analysis(LDA) to construct the rice leaf blast identification model. The experimental results show that, after preprocessing by the Savitzky-Golay method, 9 characteristic wavelengths were extracted by SPA for modeling, and the models had the best recognition effect. The prediction accuracy of the SG-SPA-SVM model and the SG-SPA-LDA model were both 98.7%.