## 3 Results
#### 3.2.1 Classification of the Images of the Dorsal Side of Fresh and Shade-dried Mint Leaves
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Predicted Class (\%)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} \\  & \multicolumn{1}{c}{**Fresh**} & \multicolumn{1}{c}{**Shade-Dried**} & & & & & \\ \hline \multicolumn{8}{c}{Color channel \(R\)} \\ lazy.KStar & 75 & 25 & Fresh & 77.5 & 0.789 & 0.750 & 0.769 & 0.551 \\  & 20 & 80 & Shade-dried & & 0.762 & 0.800 & 0.780 & 0.551 \\ rules.JRip & 85 & 15 & Fresh & & 0.944 & 0.850 & 0.895 & 0.804 \\  & 5 & 95 & Shade-dried & & 0.864 & 0.950 & 0.905 & 0.804 \\ trees.J48 & 85 & 15 & Fresh & & 0.895 & 0.850 & 0.872 & 0.751 \\  & 10 & 90 & Shade-dried & & 0.857 & 0.900 & 0.878 & 0.751 \\  & \multicolumn{8}{c}{Color channel \(G\)} \\ lazy.KStar & 70 & 30 & Fresh & 77.78 & 0.700 & 0.737 & 0.503 \\  & 20 & 80 & Shade-dried & 75 & 0.727 & 0.800 & 0.762 & 0.503 \\ rules.JRip & 85 & 15 & Fresh & & 0.739 & 0.850 & 0.791 & 0.556 \\  & 30 & 70 & Shade-dried & & 0.824 & 0.700 & 0.757 & 0.556 \\ trees.J48 & 75 & 25 & Fresh & & 0.882 & 0.750 & 0.811 & 0.657 \\  & 10 & 90 & Shade-dried & & 0.783 & 0.900 & 0.837 & 0.657 \\  & \multicolumn{8}{c}{Color channel \(B\)} \\ lazy.KStar & 100 & 0 & Fresh & 1.000 & 1.000 & 1.000 & 1.000 \\  & 0 & 100 & Shade-dried & & 1.000 & 1.000 & 1.000 & 1.000 \\ rules.JRip & 100 & 0 & Fresh & & 0.952 & 1.000 & 0.976 & 0.951 \\  & 5 & 95 & Shade-dried & & 1.000 & 0.950 & 0.974 & 0.951 \\ trees.J48 & 95 & 5 & Fresh & & 1.000 & 0.950 & 0.974 & 0.951 \\  & 0 & 100 & Shade-dried & & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance metrics of the classification of the dorsal side of fresh and shade-dried mint leaves based on selected textures of images in color channels \(R\), \(G\), and \(B\) from color space RGB. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Predicted Class (\%)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} \\  & \multicolumn{1}{c}{**Fresh**} & \multicolumn{1}{c}{**Shade-Dried**} & & & & & \\ \hline \multicolumn{8}{c}{Color channel \(L\)} \\ lazy.KStar & 70 & 30 & Fresh & 77.5 & 0.824 & 0.700 & 0.757 & 0.556 \\  & 15 & 85 & Shade-dried & & 0.739 & 0.850 & 0.791 & 0.556 \\ rules.JRip & 90 & 10 & Fresh & & 0.947 & 0.900 & 0.923 & 0.851 \\  & 5 & 95 & Shade-dried & & 0.905 & 0.950 & 0.927 & 0.851 \\ trees.J48 & 100 & 0 & Fresh & & 0.909 & 1.000 & 0.952 & 0.905 \\  & 10 & 90 & Shade-dried & & 1.000 & 0.900 & 0.947 & 0.905 \\  & 100 & 0 & Fresh & & 1.000 & 1.000 & 1.000 & 1.000 \\  & 0 & 100 & Shade-dried & & 1.000 & 1.000 & 1.000 & 1.000 \\ rules.JRip & 100 & 0 & Fresh & & 1.000 & 1.000 & 1.000 & 1.000 \\ trees.J48 & 0 & 100 & Shade-dried & & 1.000 & 1.000 & 1.000 & 1.000 \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The classification of the dorsal side of fresh and shade-dried mint leaves using models including selected image textures from color channels \(L\), \(a\), and \(b\). In the case of models including selected image textures from color channels \(X\), \(Y\), and \(Z\), a completely correct classification was not observed (Table 9). The differentiation of the structure of the dorsal side of fresh and shade-dried mint leaves allowed for distinguishing both samples at an average accuracy of up to 97.5% (100% for fresh and 95% for dried leaves) for color channel \(X\) and J48 as well as color channel \(Z\) and KStar and J48. An average accuracy of 95% was determined for models developed based on selected textures of images in color channel \(Y\) using all algorithms.