## 3 Results  
### Characteristics of the Spectral Features of Maize Seeds  
Figure 3 shows the mean reflectance spectra for 17 maize seed varieties within the wavelength range of 400-1000 nm. The general trends of the spectral curves are similar. In the wavelength range of 500-700 nm, the average spectral curve shows an upward trend for all of the 17 varieties. Moreover, the average spectral curve becomes flat within the wavelength range of 700-900 nm. A main absorption band observed nearby 1000 nm probably arises from the second overtone of O-H stretching of water interacting with protein [31]. The 400-500 nm absorbance of the spectral proves to be proportional to the protein content of the maize seeds [32]. Higher reflectance spectra are found in the near infrared region, except for the region of water absorption. These spectral differences are effective measures of the specific characteristics of each variety. Thus, these differences provide a basis for the use of hyperspectral imaging data for seed classification. Classification Results Using the Integration of Spectral and Image Features Based on the Optimal Wavelengths  
Figure 4a,b summarize the performance of models developed using the spectral features and the integration of spectral and image features for 10 random runs. The average training and test accuracies of the model using the spectral features are highly correlated with the number of selected optimal wavelengths, and the average accuracies increase significantly with the increase in the number of selected wavelengths. When the number of selected wavelengths varies between 5 and 15, the average classification accuracies of the model using the spectral features are between 68% and 94% for the training set and between 54% and 78% for the test set. For the model using the integration of the spectral features and image features, the effect of wavelength number on classification accuracy is less than that of the model using the spectral features. The model using the integration of the spectral features and image features achieves above 97% of the average training accuracy and between 75% and 85% of the average test accuracy under different wavelength numbers (varying between 5 and 15). The classification accuracies using the integration of the spectral and image features are always better than those using spectral features alone. This result further proves that the integration of the spectral and image features extracted from hyperspectral images can improve the classification accuracy of seeds reported by [PERSON] _et al._[15]. Moreover, for the same number of optimal wavelengths, the variation of accuracies of the model using the integration of the spectral and image features is smaller than that of the model using the spectral features in 10 runs. Therefore, the model using the integration of the spectral and image features is more stable than that using the spectral features in the classification accuracy. Figure 3: Mean reflectance spectra for the 17 varieties of maize seeds within the wavelength range of 400–1000 nm. To consider comprehensively the speed of data acquisition and processing as well as classification accuracy, the number of optimal wavelengths was set to 11 in this study. The optimal wavelengths selected by SPA were not exactly the same for the 10 random runs. However, several wavelengths, such as 550.7, 555.8, 610.0, 640.9, 669.2, and 743.9 nm, or their adjacent wavelengths were repeatedly selected by SPA. The 11 optimal wavelengths selected by SPA for one random run are shown in Figure 5. The most selected wavelengths for maize seed classification were located in the region of 500-750 nm, and they mainly reflected the absorption of starch and oil contained in seed [16].