# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
###### Abstract  
Wheat take-all, caused by two variants of the fungus _Gaeumannomyces graminis (Sacc.) Arx & [PERSON]. [PERSON]_, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350-1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R\({}_{990}\),R\({}_{998}\)), NDSI(R\({}_{534}\),R\({}_{742}\)) and NDSI(R\({}_{810}\),R\({}_{834}\)) was established: \(Y=134.577-70.301\) NDSI(R\({}_{990}\),R\({}_{998}\)) \(-223.533\) NDSI(R\({}_{834}\),R\({}_{742}\)) + 51.584 NDSI(R\({}_{810}\),R\({}_{834}\)) (\(R^{2}=0.743\), RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field. hyperspectral; winter wheat; take-all disease; spectral indices; estimation model +
Footnote †: journal: _Sci._**2021**, _11_, 9230. [[https://doi.org/10.3390/app11199230](https://doi.org/10.3390/app11199230)]([https://doi.org/10.3390/app11199230](https://doi.org/10.3390/app11199230))  
is the mixed seeds with diseased and residual seeds. In recent years, the take-all wheat disease has spread to vast areas and led to large quantity of lost [1]. It is of great significance to strengthen the monitoring and early-warning research on this disease to ensure the high and stable yield of wheat. Hyperspectral remote sensing technology can accurately obtain the spectral information of ground crops, especially when the plants are infected by diseases, since their external morphology and internal physiological processes will change [2]. Compared with healthy plants, the spectral characteristics of the affected plants will change to different degrees in some characteristic bands [3]. Therefore, with the rapid development of remote sensing technology, hyperspectral remote sensing technology is also increasingly being in crop disease monitoring research. [PERSON] et al. [4] used hyperspectral remote sensing data to extract spectral information of vegetation canopy, making it possible to monitor vegetation in a large range. [PERSON] et al. [5] used hyperspectral image data combined with vegetation index method to study cotton root rot and performed a large-scale graded monitoring of this disease. [PERSON] et al. [6] established the regression equation by using the relationship between the first-order differential index and the disease index and proved that hyperspectral inversion of the disease index of wheat stripe rust was completely feasible. [PERSON] et al. [7] used hyperspectral data to accurately classify wheat stripe rust leaves. [PERSON] et al.