# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 3 Results and Discussion
### Selection of Effective Wavelengths
The classification results using the SVM model were superior to the PLS-DA classification results that are shown in Table 2, and the CARS algorithm had better discrimination results than SPA and CARS + SPA. This may be because CARS extracts much larger characteristic wavelengths than the other two, and the spectrum contained sufficient active ingredients. Many studies have shown that there are differences in the internal content between the hybrid seeds and the parent, and near-infrared hyperspectral imaging could obtain the internal information of the okra seeds [1; 2; 20; 22; 24]. The classification accuracy rate of the prediction set of the SVM model that is based on CARS (94.83%) was even higher than the SVM model based on the whole band spectrum. However, the accuracy of the established classification discrimination model based on the characteristic wavelength was lower than the models based on the full spectrum. This was particularly evident in the classification results of the PLS-DA model. When comparing the two classification models based on SPA and CARS + SPA, the recognition rate was over 79%. After the characteristic wavelengths were extracted by CARS + SPA, SVM was used to establish the model whose classification accuracy rate of the modeling set and the prediction set reached 97.41% and 92.24%, respectively, which provided a new reference method for the breeding identification of hybrid okra seeds. [PERSON] et al. (2018) used SVM models to classify the variety of maize seeds [19]. Based on the optimal wavelengths, they achieved 93.85% calibration accuracy and 91.00% prediction accuracy, which was worse than our model. [PERSON] et al. (2013) established classification models to classify rice seed cultivar [39]. Their SVM models based on optimal wavelengths achieved classification accuracy rates of 97.30% and 89.47% for the modeling set and the prediction set, respectively, which was almost as good as our models. The good effect of CARS indicates that most of the spectral bands are valid for the classification of okra seeds. Excessive deletion of spectral data is likely to result in the loss of a lot of classification information, thus causing the other two methods to be unsatisfactory. The results show that the near-infrared hyperspectral technology, when combined with the chemometrics method, can identify different kinds of okra seeds quickly and effectively and the SVM model has a good classification effect. Since okra seeds and their offspring were used as research subjects, there was a transmission of genetic information between parents and their offspring. Therefore, there was some overlap between the content of hybrid seeds and the content of material between parents, which forms a barrier to the identification of spectral classifications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PLS-DA** & \multicolumn{2}{c}{**SVM**} \\ \hline \multirow{3}{*}{**SPA**} & **Parameter** & 8 & **Parameter** & (256, 27.8576) \\  & **Calibration Set** & 79.74\% & **Calibration Set** & 95.34\% \\  & **Prediction Set** & 79.48\% & **Prediction Set** & 91.55\% \\ \hline \multirow{3}{*}{**CARS+SPA**} & **Parameter** & 9 & **Parameter** & (256, 48.5029) \\  & **Calibration Set** & 81.47\% & **Calibration Set** & 97.41\% \\  & **Prediction Set** & 79.31\% & **Prediction Set** & 92.24\% \\ \hline \multirow{3}{*}{**CARS**} & **Parameter** & 9 & **Parameter** & (256, 9.1896) \\  & **Calibration Set** & 84.40\% & **Calibration Set** & 98.71\% \\ \cline{1-1}  & **Prediction Set** & 82.41\% & **Prediction Set** & 94.83\% \\ \hline \hline \end{tabular} Note: PLS-DA model’s parameter means the optimal number of LVs; SVM model’s parameter means different penalty parameters (c) and kernel function parameters (g), shown as (c, g).  
\end{table} 
Table 2: Discrimination results of the PLS-DA and SVM models based on characteristic wavelength. SPA: successive projection algorithm; CARS: competitive adaptive reweighted sampling.