# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 4 Discussion  
### Spectral Response Difference between Leaf Blade Lesions and Leaf Sheath Lesions  
The damage caused by ShB is first on leaf sheaths, and then on leaf blades when ShB occurs in rice. Figures 2 and 6 show the differences in spectral curves between the leaf blade lesions and the leaf sheath lesions. The absorption valley, reflectance peak and reflecting steep slope in the regions of blue edge, green light and red edge were more obvious in the leaf sheath lesions, though spectral curves of the leaf blade lesions and the leaf sheath lesions were similar in shape. These spectral curves are consistent with canopy spectral curves of rice infected with ShB in the report by [PERSON] et al. [18]. Meanwhile, it was observed from derivative spectral curves that the spectral response of leaf sheath lesions is different from the response of the leaf blade lesions in the red band of 670-750 nm and the near-infrared band  
Figure 5: The importance order of spectral variables with the top 50% weight at leaf lesion and leaf scale. of 915-980 nm. It is seen that rice infected with ShB, especially lesions, have a high impact on spectrum variations, which reflects the variation of absorption and reflection characteristics with wavelength. \"Three-edge\" parameters and narrow-band vegetation indices were also extracted from the data of leaf sheath lesions, and the quality of these variables was estimated by statistical analysis and the ReliefF algorithm. Figure 6 shows that the variable SDr/SDg had the power to distinguish the leaf sheath lesions from the leaf blade lesions. The average of SDr/SDg in the leaf sheath lesions was 9.665, which was higher than that of the leaf blade lesions, and it could separate them well when the threshold value of SDr/SDg was 8. The feature SDr/SDg was the ratio of the red edge area and the green peak area, which enhanced the spectral differences between the leaf sheath lesions and the leaf blade lesions. Of course, with the change in rice variety and plant growth stage, the most significant characteristic may change as well, but it will be all among the \"three-edge\" parameters and related vegetation indices corresponding to the spectral response mechanism of the leaf sheath lesions and the leaf blade lesions. The Physiological Basis of Spectral Characteristic Parameters and Vegetation Indices for Identifying Rice ShB  
Rice plants develop different symptoms after being infected with the ShB fungus [3]. These symptoms on the leaves begin with small dark green patches, then manifest in the development of lesions and color changes into brown and gray, finally causing dead and dry leaves. It is worth noting that the different symptoms of rice ShB can be observed simultaneously in various portions of the leaf. Therefore, reflectance spectra will vary with the development stage of the lesions. Thus, \"three-edge\" parameters and narrow-band indices extracted from the hyperspectral data of leaves with different symptoms can capture spectral information of rice ShB showing different symptoms. Spectral information related to these symptoms results from the changes in physiological and biochemical activities in infected leaves, such as chlorophyll, carotenoid, anthocyanin, nitrogen, carbohydrates and protein [5; 39]. Therefore, the various spectral characteristic parameters for identifying rice ShB on the leaves correspond to the physiological and chemical parameters inside the leaves. Spectral variables selected by the ReliefF algorithm were in the blue edge, green peak, yellow edge, red edge and near-infrared band, including blue edge amplitude (Db), yellow edge area (SDy), \(\text{Rg/Ro}\), \((\text{Rg-Ro})/(\text{Rg+Ro})\), and \((\text{SDnir-SDg})/(\text{SDnir+SDg})\). Shifts in these positions to longer or shorter wavelengths have been used as indicators of vegetation stress [23]. The phenomenon of red-edge shift towards blue light can be mainly attributed to the changes in chlorophyll concentration of plants under the disease stress.