## 3 Results and Discussion
### Image Based Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Pretreatment**} & \multicolumn{2}{c}{**Calibration (\%)**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Validation (\%)**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-8}  & & **Diseased** & **Sound** & **(\%)** & **Diseased** & **Sound** & **(\%)** \\ \hline \multirow{8}{*}{LDA} & RAW & 92 & 95 & 93.5 & 96 & 98 & 97 \\  & SNV & 93 & 100 & 96.5 & 98 & 98 & 98 \\  & Max normalization & 94 & 99 & 96.5 & 98 & 98 & 98 \\  & Mean normalization & 88 & 99.5 & 93.8 & 96 & 100 & 98 \\  & Range normalization & 91.5 & 99.5 & 95.5 & 98 & 100 & 99 \\  & Smoothing & 93.5 & 96.5 & 95 & 96 & 100 & 98 \\ \hline \multirow{8}{*}{QDA} & RAW & 95 & 93.5 & 94.3 & 98 & 94 & 96 \\  & SNV & 94 & 99.5 & 96.8 & 100 & 98 & 99 \\  & Max normalization & 93 & 99.5 & 96.3 & 98 & 94 & 96 \\  & Mean normalization & 92.5 & 98.5 & 95.5 & 98 & 98 & 98 \\  & Range normalization & 93 & 98.5 & 95.8 & 98 & 98 & 98 \\  & Smoothing & 95.5 & 95.5 & 98 & 96 & 97 \\ \hline \multirow{8}{*}{SVM with RBF} & RAW & 96.5 & 88.5 & 92.5 & 98 & 98 & 98 \\  & SNV & 88.5 & 99.5 & 94 & 94 & 100 & 97 \\ \cline{1-1}  & Max normalization & 91 & 99.5 & 95.3 & 98 & 98 & 98 \\ \cline{1-1}  & Mean normalization & 91 & 99 & 95 & 98 & 100 & 99 \\ \cline{1-1}  & Range normalization & 90 & 98.5 & 94.3 & 94 & 100 & 97 \\ \cline{1-1}  & Smoothing & 96 & 89 & 92.5 & 98 & 86 & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calibration and validation results of each classifier with different pretreatment methods using three subset wavelengths. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Pretreatment**} & \multicolumn{2}{c}{**Calibration (\%)**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Validation (\%)**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-8}  & & **Diseased** & **Sound** & **(\%)** & **Diseased** & **Sound** & **(\%)** \\ \hline \multirow{8}{*}{LDA} & RAW & 92.5 & 97 & 94.8 & 96 & 100 & 98 \\  & SNV & 92.5 & 100 & 96.3 & 96 & 98 & 97 \\  & Max normalization & 94.5 & 98.5 & 96.5 & 98 & 100 & 99 \\  & Mean normalization & 84.5 & 100 & 92.3 & 96 & 100 & 98 \\  & Range normalization & 86 & 99.5 & 92.8 & 94 & 100 & 97 \\  & Smoothing & 92.5 & 97 & 94.8 & 96 & 100 & 98 \\ \hline \multirow{8}{*}{QDA} & RAW & 95.5 & 93 & 94.3 & 98 & 92 & 95 \\  & SNV & 92.5 & 99.5 & 96 & 98 & 100 & 99 \\  & Max normalization & 91 & 99.5 & 95.3 & 94 & 94 & 94 \\  & Mean normalization & 91 & 99.5 & 95.3 & 94 & 98 & 96 \\  & Smoothing & 95 & 93 & 94 & 98 & 92 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calibration and validation results of each classifier with different pretreatment methods using two subset wavelengths for diseased and sound rice seed. Pixel values of samples that are less than or equal to the threshold values (0.5) were classified as diseased and they were represented in red in classification images, whereas the green color in the images represents the sound seed samples. The final color-coded images for the calibration and validation sets, based on the LDA and QDA models, are shown in Figure 7. The images clearly show that there were a few kernels in both the diseased and sound rice samples that were misclassified. This could be due to the error in the original subjective sample classification by the experts. Classification of rice seeds by humans takes a longer amount of time, and is a tedious and fatiguing process which is prone to bias and errors. The classification error is an indication that the imaging may be revealing aspects associated with disease that are not apparent to the human eye. The hyperspectral imaging technique can be a potential tool for fast and accurate classification of health/diseased and clean/dirty seeds. The next step of this study is to use a chemical assessment method to verify the results.