# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
## 4 Discussion  
In this study, ground measured spectral data were used to select appropriate spectral bands by dividing spectral interval to construct spectral index and multiple spectral indices were used to construct wheat take-all disease index estimation model. The study found that under the stress of take-all disease, wheat leaves would wither due to root death, thus forming the white spike feature. The spectral reflectance of wheat decreased with the increase of disease degree and the change was most obvious in the range of 700-900 nm. The normalized difference spectral index (NDSI) adopted in this study can better reflect the growth status of vegetation, highlight the correlation between spectral bands and disease indices and provide better characteristic parameters for the construction of disease indices estimation model. The optimal range of spectral index combination was found out by using the determination coefficient equipotential map made by NDSI and the disease index. At the same time, the band combination was selected in different zones and the autocorrelation among bands was fully considered. It was found that near infrared NDSI(R\({}_{810}\)R\({}_{834}\)) had the strongest correlation with the disease index and NDSI(R\({}_{534}\),R\({}_{742}\)) in the cross band combination was the most critical to the establishment of the model, which was included in all three models. This spectral index is composed of R\({}_{534}\) and R\({}_{742}\), which are located at the blue-green boundary and the red edge, respectively. The spectral response at this position is strong, because the change of cell fluid in plant leaves will affect the change of pigment, thus affecting the spectral reflectance and red edge changes in the visible range [18]. The spectral response of crop diseases and insect pests is related to their symptoms. By comparing G-BR and G-BR-N models, it was found that near-infrared features can effectively increase the accuracy of the model, because the reflectance of NIR band was directly affected by cell structure [2]. The chlorophyll content in wheat cells was decreased due to the wilt of wheat plants caused by take-all and the reflectance of NIR region was decreased. After increasing the NIR spectral index, the \(R^{2}\) of the model was slightly increased. The accuracy of G-BR-N ternary model was slightly higher than that of BR-N binary model. The green light feature was also one of the important characteristics affecting the physiological status of vegetation in this disease [33]. Based on the above characteristic information, a multivariate spectral index combination form with NDSI as variable was established. Using the partial least squares regression model, it was obtained as the optimal disease index estimation model by step-by-step optimization. as is shown in Equation (6), \(R^{2}\) = 0.743, RMSEP = 0.094. This model can be used to estimate the occurrence of wheat take-all in the field. Figure 5: Accuracy verification of wheat take-all prediction results. The method proposed in this study can make full use of the spectral information of different wavebands to construct a high-precision disease level assessment model, which can provide powerful technical support for precision agriculture. However, the experimental samples used in this research are limited. The next step will be to collect more experimental data to quantitatively analyze the disease status of multiple crops. In addition, the disease index estimation model proposed in this study will also be extended and applied to UAV imaging spectral data analysis to further verify its effectiveness. Conceptualization, W.G. and H.Z. ; methodology, H.Z. and W.G. ; validation, W.G. and H.Z. ; formal analysis, H.Z. ; investigation, H.Z. ; writing--original draft preparation, H.Z., Y.Y. ; writing--review and editing, H.Z., Y.Y., R.S., W.G. and M.H.A.B. ; visualization, P.D. and Q.J. ; supervision, Z.L. and Z.Y. All authors have read and agreed to the published version of the manuscript.