## 3 Results
#### 3.2.2 Classification of the Images of the Dorsal Side of Fresh and Open-Air Sun-dried Mint Leaves  
Very successful classifications were performed for the fresh and open-air sun-dried mint leaves using models built based on selected textures of the dorsal leaf side images converted to color channels \(R\), \(G\), and \(B\) (Table 10). Average accuracy reached 97.5% for models developed using the KStar, JRip, and J48 algorithms based on features of images in color channel \(B\). All the other models produced an average accuracy of 92.5% in the case of color channels \(R\) and \(G\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Predicted Class (\%)**} & \multicolumn{2}{c}{**Actual Class**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} \\  & **Fresh** & & & & & & \\ \hline \multicolumn{8}{c}{Color channel \(b\)} \\ lazy.KStar & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\
5 & 95 & Shade-dried & 0.905 & 0.950 & 0.974 & 0.951 \\ rules.JRip & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\ trees.J48 & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.974 & 0.951 \\ \hline \multicolumn{8}{c}{MCC—Matthews Correlation Coefficient.} \\ \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Predicted Class (\%)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} \\  & **Fresh** & & & & & & \\ \hline \multicolumn{8}{c}{Color channel \(X\)} \\ lazy.KStar & 90 & 10 & Fresh & 0.947 & 0.900 & 0.923 & 0.851 \\
5 & 95 & Shade-dried & 0.905 & 0.950 & 0.927 & 0.851 \\ rules.JRip & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\ trees.J48 & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.974 & 0.951 \\  & \multicolumn{8}{c}{Color channel \(Y\)} \\ lazy.KStar & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\ rules.JRip & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\ trees.J48 & 5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\  & \multicolumn{8}{c}{Color channel \(Z\)} \\ lazy.KStar & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.900 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.950 & 0.950 \\ trees.J48 & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\
5 & 95 & Shade-dried & 0.950 & 0.950 & 0.974 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The results of the classification of fresh and shade-dried mint leaves imaged on the dorsal side using models built based on selected texture parameters from color channels \(X\), \(Y\), and \(Z\) of images. In the case of the color space Lab, images of the dorsal side of fresh and open-air sun-dried mint leaves turned out to be completely different in terms of selected textures from color channels \(a\) (KStar, JRip, J48) and \(b\) (J48) (Table 11). The accuracies were equal to 100% and the values of Precision, Recall, F-Measure, and MCC were 1.000 for both classes. In the case of the color channel \(L\), the correctness of classification was slightly lower, reaching 97.5% for the model built using the JRip machine learning algorithm.