## 4 Conclusions  
The effects of background differences of data collection in the three areas were eliminated by standardizing the spectral data prior to analysis. Correlation analysis and independent sample T-tests were used to verify the feasibility of identifying the disease severity by using the spectral feature sets, which were extracted by the combination of derivative features, continuum removal features, and common vegetation indices at the ear level. The FLDA and SVM models are constructed by using the sensitive spectral feature sets as input variables, and the results show that the accuracy of the two models are ideal, although the overall accuracy of the SVM was slightly better than the FLDA, but the effect is not obvious. The LDA-SVM model combined the advantages of FLDA and SVM, and had a better identification accuracy than the FLDA or SVM models. The identification accuracy for the three angles is 88.6%, 85.7%, and 68.6%, respectively. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB, especially the first-order derivative features SDg/SDb and (SDg \(-\) SDb)/(SDg \(+\) SDb); (2) the LDA-SVM model has a good effect on identifying the severity of FHB in a complex, farmland environment; and (3) of the three angles used in the experiment, the side was the best angle for detecting FHB, followed by the front. However, the identification effect for the erect samples is poor (68.6%), which needs to be improved. The results are of great significance for identifying diseases of different severity at ear level, and provide a theoretical reference for the further study of identification of FHB at the canopy scale or regional scale. Traditionally, the methods of disease assessment have been visually investigated in the field by experienced farmers or pathologists. Visual rating is unavoidably subjective, and the error range largely depends on the personnel skills of the evaluator. The emergence of hyperspectral measurements has distinct advantages over conventional visual inspection, allowing the information to be collected repeatedly, automatically, and objectively. In this study, a convenient, simple, and effective method was used to establish a model that can accurately identify the disease severity of wheat at the filling and maturity stages, reducing the time and effort related to manual investigation. Furthermore, it is easy to implement. The method can be used to identify FHB-infected wheat at the mild disease stage and guide field management accurately. For example, in the case of FHB infections in wheat, it can identify the area of infection and prevent infections in neighboring areas. This information can help farmers to segregate the harvesting of severely affected areas of fields to avoid toxins entering the food chain. In addition, the model can be used to collect FHB data, and can provide data support and validation for some research studies. All authors contributed to the conceptualization. [PERSON] performed the formal analysis and original draft writing. [PERSON] and [PERSON] contributed to the supervision. [PERSON] and [PERSON] performed the writing, review, and editing. All authors contributed to the paper. This research was funded by Anhui Provincial Science and Technology Project (16030701091), the science and technology service program of the Chinese Academy of Sciences (KFJ-STS-ZDTP-054), Hainan Provincial Key R&D Program of China, and Natural Science Research Project of Anhui Provincial Education Department (KJ2019A0030). The authors are grateful to [PERSON] for valuable technical guidance. The authors would also like to thank [PERSON] and [PERSON] for their help with data collection. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: