Early Detection of Rice Sheath Blight Using Hyperspectral Remote Sensing

Abstract

Sheath blight (ShB) is one of the three major diseases in rice and is prevalent worldwide. Lesions spread vertically from leaf sheaths near the water surface towards the upper parts. This increases the need to develop an approach for the early detection of infection. Hyperspectral remote sensing has been proven to be a potential technology for the early detection of diseases but remains challenging due to redundant information and weak spectral signals. This study proposed a stepwise screening method of spectral features for the early detection of ShB using rice canopy hyperspectral data over two years of successive experiments. The procedure consists of the selection of key wavebands using three algorithms and a further filtration of key wavelengths and vegetation indices considering feature importance, separability, and high correlation. Sheath-blight infection can disrupt the canopy architecture and influence the biochemical parameters in rice plants. The study reported that obvious variations in the chlorophyll content and LAI of rice plants occurred under early stress of ShB, and the sensitive features selected had strong correlations with these two growth factors. By fusing support vector machine with the optimal features, the detection model for early ShB exhibited an overall accuracy of 87%, showing higher accuracy at the current level of early-stage detection of rice ShB at the field scale. The proposed method not only provides methodological support for early detecting rice ShB but also serves as a reference for diagnosing other stalk diseases in crops.