###### Abstract  
Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, [PERSON]'s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user's accuracy, and producer's accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%). Fusarium head blight; spectral feature sets; Fisher's linear discriminant analysis (FLDA); support vector machine (SVM); winter wheat +
Footnote †: journal: Applied Soft Soft 2019, 9, 3894; doi:10.3390/APP9183894
development of the FHB disease [3]. In a pandemic year, the ear disease rate is 50% to 100%, which can reduce production by 10% to 40%. In an epidemic year, the diseased rate is 30% to 50%, which can reduce production by 5% to 15% [2]. The main pathogen of wheat FHB is Fusarium graminearum, which accounts for 94.5% of the pathogens in wheat FHB in China. After the infection of wheat, the pathogen can produce a variety of fungal toxins, among which the most toxic one is deoxynivalenol (DON) [2]. DON, commonly known as vomitoxin, has acute adverse effects in animals, including food refusal, diarrhea, emesis, alimentary hemorrhaging, and contact dermatitis [4; 5]. FHB of wheat not only causes a significant drop in food production, but the DON produced by pathogens also hurts human and animal health, causing food safety problems [6; 7]. Therefore, it is important to monitor the health condition of wheat in the field pre-harvest, and to identify the diseased ears. FHB is caused by fungal infection, which affects the normal physiological function of wheat and changes the external morphology and internal physiological structure [8; 9; 10]. The primary inoculum for FHB comes from infected plant debris, on which the fungus overwinters as saprophytic mycelia. The wheat ear tissue collapses after being seriously infested, the cells disintegrate, and the external appearance of browning becomes obvious [11]. The infected kernels appear shriveled, lightweight, chalky, and covered with mycelia [7; 12]. At present, hyperspectral remote sensing technology is often used in wheat disease and pest monitoring research. In the research on remote sensing monitoring and prediction methods for wheat diseases, previous studies have focused on the yellow rust and powdery mildew. However, compared with yellow rust and powdery mildew, there are fewer studies on FHB using hyperspectral techniques. [PERSON] et al.