## 3 Results and Discussion
### Identification of Diseased Samples with Different Degrees of Severity
For the front samples, the overall accuracies of the disease severity based on the FLDA, SVM, and LDA-SVM models are 77.1%, 80.0% and 85.7%, respectively, and the Kappa coefficients are 0.639, 0.683, and 0.770, respectively. For the side samples, the overall accuracies based on the three models are 85.7%, 82.9%, and 88.6%, respectively, and the Kappa coefficients are 0.762, 0.728, and 0.808, respectively. For the erect samples, the overall accuracies of the disease based on the three models are 62.9%, 65.7%, and 68.6%, respectively, and the Kappa coefficients are 0.416, 0.424, and 0.506, respectively. The accuracy and Kappa coefficient comparison for the three models are summarized in Figure 6.  
From the above results, the identification of disease severity for the front and side samples based on three modes are generally ideal and the LDA-SVM model has the best identification effect, with accuracies of 85.7% and 88.6% and Kappa coefficients of 0.770 and 0.808, respectively. However, the classification accuracy for erect samples was slightly lower, at 68.6%. Overall, the accuracy of the SVM model is better than the FLDA model, but the effect is not obvious. Perhaps the combination of the two parameters of the kernel function coefficient and the penalty coefficient of the SVM is not optimal. The parameters are selected using manual methods, which are not only inefficient with low classification accuracy, but also prone to overfitting [42]. There are a lot of feature variables and excessive variables that not only increase the difficulty of calculation, but also make the interpretability of the model worse. Therefore, combining the advantages of the FLDA and SVM algorithms, the FLDA is used to reduce the dimension of the original feature variables, and the resulting low-dimensional eigenvectors are used as the input variables of the SVM model, effectively improving the accuracy of the model. In addition, from the perspective of user's accuracy and producer's accuracy, the accuracy of the LDA-SVM model reach more than 80% or even 100% for the front and side angles, which is obviously better than the FLDA model or SVM model. The producer's accuracy values of healthy samples are 92.3%, 81.8%, and 81.8% for the front, side, and erect angles, respectively. This indicates that the LDA-SVM model can ideally identify discoloration or maturation from FHB-infected ears. The classification accuracy for side samples is higher than the front, and for the erect samples is the lowest. Therefore, we can conclude that the side is the best angle to detect FHB, followed by the front. The reason for that is the fact that the classification accuracy of ears infected with FHB is affected by the measurement angle of the ear. The result is related to the number of input features of the identification model and the ability of the features to identify the FHB. The number of sensitive spectral features obtained for the side samples is more than the front, whereas the number of sensitive features for erect samples is only half that of the front and side samples. The sensitive spectral features for the side samples are superior to the front, and the front samples are superior to the erect samples with respect to the ability to identify FHB. In order to evaluate the contribution of each selected feature to the classification in the model, the weights were calculated using the relief algorithm (Figure 7) [43]. The results show that the features, including (\(\text{SDg}-\text{SDb}\))/(\(\text{SDg}+\text{SDb}\)), \(\text{SDg}\)/\(\text{SDb}\), NRI, SDb, SIPI and PSRI, have important weights at the side angle; the features of \(\text{SDg}\)/\(\text{SDb}\), (\(\text{SDg}-\text{SDb}\))/(\(\text{SDg}+\text{SDb}\)), (\(\text{SDr}-\text{SDy}\))/(\(\text{SDr}+\text{SDy}\)), SIPI, and  
Figure 6: Comparison curve of model accuracy and kappa coefficient. NRI have important weights at the front angle; the features of SDg/SDb and (SDg - SDb)/(SDg + SDb) have important weights at the erect angle.