## 3 Results and Discussion
### SSR Detection Based on Optimal Features  
With the aim of detecting SSR, the optimal wavelengths selected by RF-CA, SPA-CA, and SFS-CA, respectively, were used as inputs of classifiers to build discriminant models. Figure 5 shows the classification performance of the ELM based on three different feature selection methods. In general, it presented a similar changing pattern using these three feature selection methods. However, the parameter of the ELM model, hidden nodes, would significantly impact the detecting performance, which indicated the importance of parameter optimization. It showed that the increasing tendency of classification accuracies with increasing hidden nodes in the range of 1 to 5, and gradually reached a steady pattern. The best detecting accuracies of ELM-SPA-CA and ELM-SFS-CA were both 93.7% with the hidden nodes of 13 and 10, respectively, while ELM-RF-CA achieved the best overall classification accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Selection** & **Number** & **Wavelength (nm)** \\ \hline RF & 16 & 458, 660, 662, 680, 682, 742, 743, 747, 791, 792, 797, 822, 862, 927, 952, 987 \\ SPA & 16 & 456, 464, 468, 474, 496, 631, 650, 667, 678, 690, 700, 710, 947, 1000, 1013, \\ SFS & 10 & 455, 469, 671, 682, 745, 747, 786, 801, 865, 998 \\ RF-CA & 3 & 458, 660, 742 \\ SPA-CA & 4 & 456, 678, 710, 947 \\ SFS-CA & 3 & 455, 671, 747 \\ \hline \hline \end{tabular} Note: NB, RF, the random frog algorithm; SPA, successive projection algorithm; SFS, sequential forward selection; CA, correlation analysis.  
\end{table} 
Table 1: Optimal wavelengths selected by RF, SFS and SPA. Figure 4: Correlation analysis (CA) among optimal wavelengths selected by (**a**) random frog (RF), (**b**) successive projections algorithm (SPA) and (**c**) sequential forward selection (SFS), respectively. accuracy of 90.5% with hidden nodes of 7. The results demonstrated that the discriminating performance using features from SPA-CA and SFS-CA was relatively better than those from RF-CA. The analysis above denoted that the three optimal wavelengths selected by SPA-CA and SFS-CA might involve relatively more important spectral signatures of SSR. The classification discriminant results could vary from different feature selection methods and model parameters. Therefore, it was also necessary to investigate the differences of discriminant ability among different classifiers, including ELM, KNN, LDA, NB and SVM. Table 2 presents detecting results from the different combination of classifiers and feature selection methods. The results from KNN, LDA, and NB were comparable and were all lower than 90% through different selected wavelengths were used. However, the ELM and SVM models could achieve outperformed detecting accuracies up to 93.7% and 94.7%, respectively, implying that the classifiers could impact the detecting result to a more extent than that of feature selection methods in this study. The similar detecting accuracy (95%) could also be found in SSR detection in oilseed rape stems with 16 optimal wavelengths as the model input [15]. In this study, only three wavelengths were used for SSR detection, which could be more suitable for high-speed online implementation due to the advantages of analyzing low-scale spectral images. It was not conclusive to compare the performance of classification models based on just the overall classification accuracy. Therefore, step-by-step discrimination results were presented as the confusion matrix of the healthy and infected leaves from different combinations of classifier and feature selection