###### Abstract
(2015) used hyperspectral imaging techniques to identify the FHB of wheat kernels by spectral analysis and image processing. The constructed linear discriminant analysis SVM and back propagation (BP) neural network models have good success in identifying FHB-infected kernels, with accuracy rates above 90% [13]. [PERSON] et al. (2018) constructed a classification model based on texture parameters of hyperspectral images to identify the infected kernels, and kernels positioned on the ventral side were classified with 100% accuracy [6]. [PERSON] et al. (2019) used hyperspectral imaging on individual kernels and linear discriminant analysis models to differentiate between healthy and Fusarium-damaged kernels based on the mean reflectance values of the interior pixels of each kernel at four wavelengths (1100, 1197, 1308, and 1394 nm). The results indicate the strong potential of near-infrared hyperspectral imaging in estimating Fusarium damage [14]. The previous studies mainly focused on classification of diseased kernels using hyperspectral imaging or identification of diseased ears under laboratory conditions, and therefore, cannot be applied to FHB identification under field conditions. There is limited literature available in the field environment [15]. The literature that is available is incomplete--it relates only to identification of the disease, and either the ideal identification effect is not achieved or the disease severity is not classified. [PERSON] et al. (2011) used hyperspectral imagery and the derived head blight index (HBI), which uses spectral differences in the ranges of 665-675 nm and 550-560 nm, as a suitable outdoor classification method for the identification of head blight; the mean hit rates were 67% during the study period [16]. [PERSON] et al. (2018) classified wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in wild fields, with the classification accuracy reaching 74.3% [17]. [PERSON] et al. (2018) implemented a hyperspectral line imager for online measurement of FHB wheat in the field, and RGB photos collected from the ground truth plots were used to assess crop disease incidence (the number of individual infected ears in relation to the healthy individuals). The study achieved good accuracy (82%) but did not identify the severity of the disease [15]. This study will make up for the shortcomings of previous studies, using non-imaging hyperspectral techniques to target individual ears to achieve identification and classification of the severity of ear disease. The algorithm of [PERSON]'s linear discriminant analysis (FLDA) performs dimensionality reduction on the original high-dimensional eigenvectors. By linearly combining each dimension of an existing high-dimensional eigenvector, the factors that have no effect on the classification are eliminated. Information that facilitates classification is completely preserved, making the final classification easier and more efficient. At the same time, the dimension of the eigenvector is reduced, which effectively reduces the computational complexity in the classification [18]. The goal of the support vector machine (SVM) algorithm is to seek the optimal combination of learning accuracy and learning ability using finite sample information, which can avoid the problem of falling into local extremum, which often occurs in neural networks [19]. After literature research, it was found that the SVM model has high application value in the classification and identification of small-scale disease data [20]. Therefore, we tried to apply the SVM algorithm to the identification of wheat FHB at the individual ear level. In order to further improve the accuracy of the model, the LDA-SVM model was constructed, combining the advantages of FLDA and SVM. Research on remote sensing monitoring of crop diseases is the basis for disease monitoring at the regional scale. According to literature research, the spectral features of winter wheat infected with FHB at the ear level have not been systematically studied.