# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 1 Introduction  
Sheath blight (ShB), caused by _Rhizoctonia solani_ AG1-IA, is one of the three major diseases in rice. The disease can cause yield losses of 10% to 30%, with most severe loss of up to 50% [1; 2; 3; 4]. The successful control of ShB is crucial to the profitable of rice production [5]. Accurate detection of rice ShB is the key to develop effective management strategies for control of this disease. Remote sensingtechnology can be a useful tool to quickly, efficiently and accurately detect and monitor the occurrence and development of ShB and other crop diseases and insect pests [6; 7; 8]. Studies have been conducted using RGB, multispectral and hyperspectral images to detect ShB in rice. Researchers often use visual technology and neural network methods to extract color and texture features of ShB at different levels of disease, and investigate the feasibility of image processing technology in the detection of ShB in rice. However, the results of these studies are easily affected by background, light, growth period, etc. [9; 10; 11]. Multispectral sensors contain more wavebands such as near-infrared wavelengths, which are used for calculation of some broad-band vegetation indices. [PERSON] et al. [12] made an early investigation to examine the applicability of multispectral remote sensing to detect rice ShB. In this case, it was discovered that the multispectral sensors with four broader bands were not able to accurately discriminate plants with light infections. In addition, [PERSON] et al. [13] also used RGB and multispectral sensors to assess disease severities of rice ShB, and showed that the normalized difference vegetation index (NDVI) could better distinguish the disease degree than other indices but the accuracy was not high. Hyperspectral technology has been proved to be a useful tool to detect crop diseases. The occurrence of diseases may cause modifications to the biophysical and biochemical characteristics of plant tissue, which results in changes in reflectance of leaf tissue. Hyperspectral remote sensing has advantages of high spectral resolution with many wavebands. Specific wavebands can be selected for the detection of diseases with different symptoms [7; 14; 15]. Most application strategies have been on the establishment of identification models combined with hyperspectral data reduction methods [16; 17; 18; 19]. These models contain partial least squares-discriminant analysis, linear discriminant analysis, a back propagation neural network or a support vector machine. Principal component analysis and the minimum noise fraction transformation method are adapted as data reduction methods. However, spectral response characteristics of plant leaves or canopy infected with ShB are not clearly analyzed in this process, and these discriminant models are largely dependent on specific data. The development of classification models is useful and important in advancing portable tools for rapid assessment of detecting plant diseases. However, the prerequisite for achieving a model with high accuracy is the construction of specific and optimal indicators for the disease of interest. These indexes can be reflectance of a waveband or some bands, spectral characteristic position variables, or some vegetation indices sensitive to the disease. This depends on the detailed exploration of spectral response mechanism for the disease. The progression and spread of rice ShB can be divided into three types, namely, \"inward spread\", \"vertical spread\" and \"horizontal spread\" [3]. When rice is infected by the ShB fungus, lesions with fuzzy edges initially appear on the leaf sheath near the surface water line. Then, runner hyphae infect and spread vertically upwards along leaf sheaths and leaf blades, and horizontally from leave to leave and from plant to plant [1]. Plant leaves or canopies with ShB are the main field of view for remote sensing. In this study, we used the ground-based imaging spectrometer to obtain the hyperspectral remote sensing images of rice leaves naturally infected with ShB.