# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
## 3 Results
### Wheat Take-All Disease Model Based on Spectral Index  
Bands from the same wavelength range tend to have high correlation and accuracy loss could occur when regression model is built using hyperspectral bands without considering the correlation between them. Using the NDSI spectral index composed of bands from different wavelength ranges, binary and ternary variable regression models were constructed based on PLSR method and the results of all range combinations are shown in Table 3. The binary and ternary models with the highest coefficient of determination were selected as the optimal model combinations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Range Name** & **Code** & **Spectral Index** & \(R^{2}\) \\ \hline Blue & B & NDSI(R\({}_{510}\),R\({}_{542}\)) & 0.649 \\ Green & G & NDSI(R\({}_{590}\),R\({}_{598}\)) & 0.590 \\ Red & R & NDSI(R\({}_{706}\),R\({}_{710}\)) & 0.556 \\ Near Infrared & N & NDSI(R\({}_{810}\),R\({}_{834}\)) & 0.828 \\ \hline Blue-Green & BG & NDSI(R\({}_{510}\),R\({}_{562}\)) & 0.616 \\ Blue-Red & BR & NDSI(R\({}_{534}\),R\({}_{742}\)) & 0.793 \\ Blue—Near Infrared & BN & NDSI(R\({}_{534}\),R\({}_{942}\)) & 0.677 \\ Green—Red & GR & NDSI(R\({}_{554}\),R\({}_{742}\)) & 0.751 \\ Green—Near Infrared & GN & NDSI(R\({}_{558}\),R\({}_{942}\)) & 0.636 \\ Red—Near Infrared & RN & NDSI(R\({}_{634}\),R\({}_{878}\)) & 0.522 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimum spectral index and its determination coefficient.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Range Combination** & \(R^{2}\) & **ID** & **Range Combination** & \(R^{2}\) \\ \hline
1 * & B-GR & 0.758 & 13 & B-G & 0.298 \\
2 & B-GN & 0.691 & 14 & B-R & 0.362 \\
3 & B-RN & 0.643 & 15 & B-N & 0.316 \\
4 * & G-BR & 0.759 & 16 & G-R & 0.413 \\
5 & G-BN & 0.731 & 17 & G-N & 0.286 \\
6 & G-RN & 0.658 & 18 & R-N & 0.372 \\
7 & R-GB & 0.351 & 19 * & B-GR-N & 0.762 \\
8 & R-BN & 0.732 & 20 & B-GN-R & 0.692 \\
9 & R-GN & 0.691 & 21 & B-RN-G & 0.736 \\
10 & N-BG & 0.300 & 22 * & G-BR-N & 0.760 \\
11 * & N-BR & 0.755 & 23 & G-BN-R & 0.732 \\
12 * & N-GR & 0.750 & 24 & R-GB-N & 0.374 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Binary and ternary spectral indices combination models and their determinants.  
As can be seen from Table 3, the spectral indices combination of cross color band combination has played a good fitting effect. Among them, the combination of G-BR, N-BR, N-GR had a better fitting effect and B-GR-N and G-BR-N had a better fitting effect in the ternary combination. There were 24 combinations of binary and ternary variable models. The first six groups with determination coefficient \(R^{2}\) above 0.75 were selected for further analysis and the PLSR model was used to establish the estimation model of wheat take-all disease indices. The predicted value of the disease indices were obtained and the determination coefficient between it and the measured value was calculated. The fitting formulas of the inversion model of the first six groups of disease indices with high determination coefficient were shown in Table 4.