## 3 Results and Discussion  
### Spectral Profiles and Selection of Optimal Wavelengths  
The average spectra of the sound and diseased rice samples, with SNV pretreatment, are shown in Figure 3. Mean spectra of healthy and diseased seeds in Figure 3 clearly show the spectra are distinguishable, indicating that the mean spectra do not cause error due to non-homogenous spectral grouping as explained by [PERSON] et al. (2018) [PERSON] et al. (2018). Distinguished wavelength regions were determined by removing a constant offset term. Aside from the intersections of spectral intensities at around 480 and 760 nm, the sound and diseased rice samples exhibited visually obvious differences throughout entire spectral region under investigation. The obvious differences were generally indicative of the more reddish and less blue color of the diseased rice. Intensity differences in the region between 800 and 1000 nm were also observed, possibly due to the changes in chemical composition of the seed due to infection [PERSON] et al. (2018); [PERSON] et al. (2018). In common with the observation of average spectra, one main interval of wavelengths (from 396 to 416 nm) and minor intervals of wavelengths (from 596 to 646 nm) were observed. This result indicates that the violet-blue and orange-red regions are crucial wavelengths to classify the discolored, diseased rice from sound seed using discriminant analysis methods. It should be noted that the SFS selected wavebands match well with the spectral differences between two different groups of seeds as shown in Figure 3. It is interesting to note that despite a significant visual difference in spectral features of sound and diseased seeds in the NIR region (800-1000 nm), the frequency of these being included among the selected wavelengths is relatively lower than those selected in the visible region by SVM. However, SFS analysis with LDA and QDA classifiers along with different preprocessing methods selected the third highest frequency (optimal) wavelengths in the NIR region as shown in Table 2. The reason for differently selected wavebands is that discriminant analysis focuses on minimizing variance among group variables (between-scatter matrix) and maximizing class separation (between-scatter matrix). Therefore, NIR regions with relatively small variance were selected by discriminant analysis. The result of the selected optimum wavelengths by each classifier with pretreatments is shown in Table 2. It is a similar result to a previous study regarding fungal infection in rice panicles in that the blue, green  
Figure 2: Key procedure steps used for the discrimination of diseased rice seed. and red regions were also used for important feature discrimination of diseased rice [9]. To choose the number of wavelengths, Figure 4 presents the accuracy at each number of features from 1 to 10. As a result, all of the classifiers obtained a high accuracy with \(>\)93%. Moreover, all of the classifiers with pretreatments have similar high accuracy when using over two wavelengths. However, for all classification techniques, raw and smoothed data attained slightly higher accuracy than those models developed with other preprocessing methods. It is important to keep the optimal number of variables at a minimum. However, because a lower number of optimal variables can reduce performance accuracy in many cases, each application should carefully consider the tradeoffs. In addition to the accuracy issue, if the system must consider a greater number of wavelengths, it will be more expensive and take a longer time for data processing due to the increased number of device sensors and increased volume of measurement data. As shown in Figure 4, single wavelengths can classify sound and diseased rice samples with high accuracy. However, the use of a single feature can be highly affected by such things as instrumental variables, signal-to-noise ratio and environmental noise.