Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages

Abstract

BackgroundRice blast, which is prevalent worldwide, represents a serious threat to harvested crop yield and quality. Hyperspectral imaging, an emerging technology used in plant disease research, is a stable, repeatable method for disease grading. Current methods for assessing disease severity have mostly focused on individual growth stages rather than multiple ones. In this study, the spectral reflectance ratio (SRR) of whole leaves were calculated, the sensitive wave bands were selected using the successive projections algorithm (SPA) and the support vector machine (SVM) models were constructed to assess rice leaf blast severity over multiple growth stages.ResultsThe average accuracy, micro F1 values, and macro F1 values of the full-spectrum-based SVM model were respectively 94.75%, 0.869, and 0.883 in 2019; 92.92%, 0.823, and 0.808 in 2021; and 88.09%, 0.702, and 0.757 under the 2019–2021 combined model. The SRR–SVM model could be used to evaluate rice leaf blast disease during multiple growth stages and had good generalizability.ConclusionsThe proposed SRR data analysis method is able to eliminate differences among individuals to some extent, thus allowing for its application to assess rice leaf blast severity over multiple growth stages. Our approach, which can supplement single-stage disease-degree classification, provides a possible direction for future research on the assessment of plant disease severity during multiple growth stages.