# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 3 Results
### The Relevance of Each Variable in the Spectral Library to Rice ShB in Diseased Leaf Blade Fractions  
The above spectral responses of rice ShB and statistical results of \"three-edge\" parameters and 11 narrow-band vegetation indices suggest that these variables can be considered for the spectral feature library of rice ShB. However, these parameters may contain specific spectral information of the disease or duplicate information, or both. The Relieff algorithm can link the predictor and the dependent variable to determine the importance of each predictor to the dependent variable. This importance is measured by the size of the weight. The higher the weight, the stronger the ability of this parameter to identify the disease. Figure 5 illustrated parameters with weights in the top 50% arranged in a descending order. Weights of (\(\text{Rg}-\text{Ro}\))/(\(\text{Rg}+\text{Ro}\)), (\(\text{Sdy}-\text{SDb}\))/(\(\text{Sdy}+\text{SDb}\)), Rg/Ro, NRI, RVSI, Sdy, Sdy/SDb, PRI, NBNDVI, blue edge amplitude (Db), red edge amplitude (Dr), (SDnir - SDr)/(SDnir + SDr) and triangular vegetation index (TVI) were close to or exceeded 0.5, and among these, the vegetation index (\(\text{Rg}-\text{Ro}\))/(\(\text{Rg}+\text{Ro}\)) had the highest weight of 0.747. Except for blue edge amplitude and red edge amplitude, other predictors are all mathematical formulas combined by two or more single spectral features. This implies that indices formed by multiple single spectral features can expand disease information, eliminate noise and help to improve the ability to recognize rice ShB. In addition, these parameters corresponded to those with different range of values between healthy and diseased leaf blade fractions.  
Figure 4: Descriptive statistics of narrow-band vegetation indices.  
Capability of Detecting Rice ShB at the Leaf Scale by the Spectral Feature Library Constructed by Disease Symptom Fractions  
Rice leaf lesions are difficult to extract due to the mutual shielding between the leaves, while leave samples are available from high spatial resolution images. Therefore, the examination of the application of spectral variables based on leaf lesions was given as follows at the leaf scale. Figure 5 presents the selected spectral parameters with top 50% weights by the ReliefF algorithm based on hyperspectral data of rice leaves. Compared with weights of various indicators at the lesion scale, the weight value of each feature at the leaf scale was significantly lower, with the maximum weight value being only 0.224. Furthermore, the characteristics with the top 50% weights were also different. The feature with the highest weight was Rg/Ro, not (Rg - Ro)/(Rg + Ro), which ranked fourth. Yellow edge area (SDy) and RVSI were both very important in the two kinds of data, ranking fifth and sixth. Other variables selected were blue edge amplitude (Db), blue edge area (SDb), red valley amplitude (Ro), NRI, PRI, (SDy - SDb)/(Sdy + SDb), (Sdnir - SDr)/(Sdnir + SDr), (Sdnir - SDb)/(Sdnir + SDb), and (SDr - SDy)/(SDr + SDy). It can be seen that the information of healthy proportions in the whole leaf will weaken and average out disease information, resulting in the importance differences of attributes.  
The discrimination model of rice ShB in the leaves was established to validate the reliability of features by the decision tree algorithm. The model took Rg/Ro with the highest weight as the predictive variable and used 10-fold cross validation. The average error rate of the model was 4.5%, and the standard error of the means was 3.0%. This demonstrates that the spectral library of \"three-edge\" parameters and narrow-band vegetation indices has certain recognition significance for rice ShB at the leaf or canopy scale.