# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
###### Abstract
[8] estimated the severity of wheat stripe rust by using principal component analysis and the empirical method of vegetation indices. [PERSON] et al. [9] used non-imaging hyperspectral data combined with RGB images to analyze the applicability of multiple vegetation indices to distinguish wheat stripe rust with different disease levels. [PERSON] et al. [10] used hyperspectral data to classify the disease degree of rice leaf blast and achieved good results. [PERSON] et al. [11] used wheat spectral data collected by ASD non-imaging spectrometer to conduct experiments and proved that hyperspectral data could effectively monitor wheat take-all. [PERSON] et al. [12] built a winter wheat take-all monitoring model by using the relationship between spectral index and disease index and successfully analyzed the degree of wheat disease, which also provided a solution for monitoring winter wheat and other crop diseases on the plot scale. It can be seen from the above research results that hyperspectral technology has a good application potential in the identification of agricultural disease distribution and grading, especially wheat take-all, but the accuracy of the model and the effect of application still need to be optimized and explored [13]. Take-all disease causes great damage to the roots of wheat, obstructing the transport of nutrients and water. This leads to a lack of nutrients in the upper part of the wheat to produce white spike symptom [14]. The spectral characteristics will be significantly different in the visible and near infrared regions, which makes it possible to monitor infected crops through non-imaging hyperspectral spectroscopy. [15]. Different diseases have specific spectral responses. For crop diseases and insect pest identification based on non-imaging hyperspectral data, more attention should be paid to the selection of sensitive wavelengths corresponding to various diseases and insect pests and the construction of specific diseases and insect pests indices [16]. Researchers targeted disease-specific data and combined different wavelength data to construct specific disease and pest identification index [17; 18]. For example, normalized difference vegetation index (NDVI), which is suitable for the analysis of green vegetation and has been applied in the analysis of stress of various diseases and insect pests. On the basis of this, many researchers have defined improved vegetation index, such as photochemical reflectance index [19] (PRI), structure-insensitive pigment index [20] (SIPI) and normalized difference infrared index [21] (NDII), which are based on the NDVI formula. In this paper, the normalized difference spectral index (NDSI) was used to calculate the spectral index of the whole bands based on the NDVI formula to highlight the characteristic information of the hyperspectral band. According to the bands of the constructed spectral index, the most bands for the identification of wheat take-all can be analyzed from different locations in order to realize the disease index estimation. In this study, the winter wheat field infected with wheat take-all was taken as the research object and the NDSI was calculated according to the spectral data collected by hyperspectral instrument and the determination coefficients of each spectral index and disease index of different band combinations were calculated. The spectral index combinations that could be used to estimate the disease index in the field were selected by dividing the band range. Partial least squares regression was used to screen out a wheat take-all disease estimation model based on the combination of multiple spectral indices containing bands from different wavelength groups, so as to achieve the purpose of monitoring wheat take-all.