## 3 Results and Discussion
### Image Based Classification  
As shown in Tables 3 and 4, all four classifiers perform with good accuracy (>92%) for the validation set in all cases. Average classification accuracies of 94% and 96% for the calibration and validation sets, respectively, are achieved when using two wavelengths. When using three wavelengths, the classifiers performed approximately 1% better, with average classification accuracies of 95% and 97% for the calibration and validation sets, respectively. The best performance model was LDA with an accuracy of 96.5% and 99% with max normalization using two wavelengths. The QDA with SNV classifier achieved the best classification accuracies of 96% and 99% for calibration and validation, respectively, when using three wavelengths. The performances of the other classifiers were inferior compared to QDA with SNV but still presented high accuracy for both calibration and validation sets. These models can be used for a practical system. For using these results on other systems, the LDA and QDA with a smoothing model are suggested since the system chooses optimal wavelengths from diverse regions, not concentrated in one region. Furthermore, previous studies have suggested that using optimal wavelengths for multispectral systems will help retain most of the original information of the samples [15; 21; 22; 34]. However, if a system uses similar wavelength regions, it cannot provide diverse information regarding the target. Hence, by using optimal wavelengths from various regions, it can contain the most possible original information of the target and prevent negative influence resulting from high collinearity. For developing a system for detecting diseased rice, the LDA and QDA with a smoothing model is suggested since wavelengths for LDA and QDA were selected, respectively, in violet, yellow and red  
Figure 5: The decision boundaries are visualized by using two wavelengths. (**a,b**) show LDA with range normalization and QDA with range normalization. (**c,d**) show SVM with raw data and RBF SVM with raw data. (**e,f**) show RBF SVM with range normalization and QDA with SNV. Figure 6: The decision boundaries are visualized in raw data for classification between sound and diseased rice samples by using (**a**) SVM and (**b**) RBF SVM classification methods with three features. regions (396, 578 and 741 nm, respectively) and violet, red, and NIR regions (420, 631 and 990 nm, respectively). This result implies that violet and red regions (yellow, red, orange) have the most significance for identifying diseased rice seed. In other studies that have used hyperspectral image analysis, colormaps are usually presented with PCA, spectrum angle mapper and normalized cross correlation since they lead to identification of the target [35; 36]. [PERSON] et al. (2009) [37] and [PERSON] et al. (2010) [38] depicted maize kernel hardness and sprout damage in Canada western red spring wheat via PCA score. Protein content prediction in single wheat kernels was reported by colormap image with a PLS model [39], and a prediction model based on PLS and genetic algorithm visualized total acid and moisture content in vinegar cultures [40]. These methods are a good way to explain the variance with images in the multivariable data. However, feature extraction methods such as PCA and PLS use full wavelength data, which leads to a longer processing time compared to methods using data consisting of only a few wavelengths. As the purpose of this current study was to minimize the number of spectral bands to increase the detection speed for real-time measurements, it was necessary to select the lowest possible number of spectral variables and to use spectrum data without signal decomposition. Thus, diseased and clean seeds are represented with only two colors in the final detection images that resulted from using either two or three spectral bands. Figure 7: Visualization of classification image with smoothing pretreatment by using (**a**) two and (**b**) three wavelengths.