## 3 Results
#### 3.2.2 Classification of the Images of the Dorsal Side of Fresh and Open-Air Sun-dried Mint Leaves
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{3}{c}{**Predicted Class (\%)**} & \multicolumn{3}{c}{**Average**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**MCC**} \\
**Fresh** & **Open-Air Sun-dried** & **Actual Class** & \multicolumn{1}{c}{**Accuracy (\%)**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**MCC**} \\ \hline \multicolumn{10}{c}{Color channel _R_} \\ lazy.KStar & 95 & 5 & Fresh & 0.905 & 0.950 & 0.927 & 0.851 \\ \multicolumn{10}{c}{} \\ lazy.KStar & 10 & 90 & Open-air sun-dried & 92.5 & 0.947 & 0.900 & 0.923 & 0.851 \\ rules.JRip & 100 & 0 & Fresh & 0.870 & 1.000 & 0.930 & 0.860 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 1.000 & 0.850 & 0.919 & 0.860 \\ \multicolumn{10}{c}{} \\ trees.J48 & 15 & 85 & Open-air sun-dried & 92.5 & 1.000 & 0.850 & 0.919 & 0.860 \\ \multicolumn{10}{c}{} \\ lazy.KStar & 95 & 5 & Fresh & 0.905 & 0.950 & 0.927 & 0.851 \\ \multicolumn{10}{c}{} \\ lazy.KStar & 10 & 90 & Open-air sun-dried & 92.5 & 0.947 & 0.900 & 0.923 & 0.851 \\ rules.JRip & 100 & 0 & Fresh & 0.870 & 1.000 & 0.930 & 0.860 \\ \multicolumn{10}{c}{} \\ trees.J48 & 15 & 85 & Open-air sun-dried & 92.5 & 1.000 & 0.850 & 0.919 & 0.860 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 0.870 & 1.000 & 0.930 & 0.860 \\ \multicolumn{10}{c}{} \\ lazy.KStar & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{10}{c}{} \\ \multicolumn{10}{c}{} \\ lazy.KStar & 5 & 95 & Open-air sun-dried & 97.5 & 1.000 & 0.950 & 0.974 & 0.951 \\ rules.JRip & 95 & 5 & Fresh & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{10}{c}{} \\ rules.JRip & 0 & 100 & Open-air sun-dried & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\ trees.J48 & 95 & 5 & Fresh & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{10}{c}{} \\ trees.J48 & 0 & 100 & Open-air sun-dried & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The distinguishing of fresh and open-air sun-dried mint leaves imaged on the dorsal side using models developed based on selected textures from color channels \(L\), \(a\), and \(b\) from color space Lab. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{3}{c}{**Predicted Class (\%)**} & \multicolumn{3}{c}{**Average**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**MCC**} \\  & **Fresh** & **Open-Air Sun-dried** & **Actual Class** & \multicolumn{1}{c}{**Accuracy (\%)**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**MCC**} \\ \hline \multicolumn{10}{c}{} \\ lazy.KStar & 95 & 5 & Fresh & 0.864 & 0.950 & 0.905 & 0.804 \\ \multicolumn{10}{c}{} \\ rules.JRip & 15 & 85 & Open-air sun-dried & 0.944 & 0.850 & 0.895 & 0.804 \\ \multicolumn{10}{c}{} \\ rules.JRip & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 0.950 & 0.974 & 0.951 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 100 & Open-air sun-dried & 97.5 & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 0.950 & 0.974 & 0.951 \\ \multicolumn{10}{c}{} \\ trees.J48 & 100 & 100 & Open-air sun-dried & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{10}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The differentiation of fresh and open-air sun-dried mint leaves in terms of selected image textures of the dorsal leaf side based on models built for color channels \(R\), \(G\), and \(B\) from color space RGB. The classification of fresh and open-air sun-dried mint leaves based on image textures of the dorsal leaf side revealed 100% correctness in the case of color channel \(Z\) and the J48 algorithm (Table 12). For channel \(Z\), other algorithms (KStar, JRip) also produced a high average accuracy of 97.5%.