## 3 Results
#### 3.2.1 Classification of the Images of the Dorsal Side of Fresh and Shade-dried Mint Leaves  
In the case of color channels \(R\), \(G\), and \(B\) from color space RGB, the images of the dorsal side of shade-dried mint leaves were distinguished from the fresh ones with an average accuracy of 75% for a model built based on selected textures from color channel \(G\) using the KStar algorithm to 100% in the case of color channel \(B\) and KStar (Table 7). For the model producing 75% correctness, fresh leaves were classified with an accuracy of 70% and shade-dried samples with an accuracy of 80%. The values of Precision, Recall, and F-Measure were in the range of 0.700-0.800, and MCC was equal to 0.503 for both classes. These results are relatively low compared with the 100% accuracy and values of other metrics of 1.000 for one model including selected texture parameters extracted from images in color channel \(B\) \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Predicted Class (\%)**} & \multirow{2}{*}{**Actual Class**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} \\ \cline{5-6}  & & & & & & & & \\ \hline \multicolumn{6}{c}{Color channel \(X\)} \\ lazy.KStar & 100 & 0 & Fresh & 100 & 1.000 & 1.000 & 1.000 & 1.000 \\  & 0 & 100 & Open-air sun-dried & 1.000 & 1.000 & 1.000 & 1.000 \\ rules.JRip & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\  & 5 & 95 & Open-air sun-dried & 95 & 0.950 & 0.950 & 0.950 & 0.900 \\ trees.J48 & 95 & 5 & Fresh & 0.950 & 0.950 & 0.950 & 0.900 \\  & 5 & 95 & Open-air sun-dried & 95 & 0.950 & 0.950 & 0.950 & 0.900 \\  & & & Color channel \(Y\) & & & & \\ lazy.KStar & 100 & 0 & Fresh & 100 & 1.000 & 1.000 & 1.000 \\  & 0 & 100 & Open-air sun-dried & 1.000 & 1.000 & 1.000 & 1.000 \\ rules.JRip & 95 & 5 & Fresh & 1.000 & 0.950 & 0.974 & 0.951 \\ trees.J48 & 0 & 100 & Open-air sun-dried & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\  & 0 & 100 & Open-air sun-dried & 97.5 & 0.952 & 1.000 & 0.976 & 0.951 \\  & & & Color channel \(Z\) & & & & \\ lazy.KStar & 95 & 5 & Fresh & 0.905 & 0.950 & 0.927 & 0.851 \\  & 10 & 90 & Open-air sun-dried & 92.5 & 0.947 & 0.900 & 0.923 & 0.851 \\ rules.JRip & 90 & 10 & Fresh & 0.947 & 0.900 & 0.923 & 0.851 \\  & 5 & 95 & Open-air sun-dried & 92.5 & 0.905 & 0.950 & 0.927 & 0.851 \\ trees.J48 & 100 & 0 & Fresh & 0.952 & 1.000 & 0.976 & 0.951 \\  & 5 & 95 & Open-air sun-dried & 97.5 & 1.000 & 0.950 & 0.974 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification performance metrics for fresh vs. open-air sun-dried mint leaves imaged on the ventral side based on models including selected textures from color channels \(X\), \(Y\), and \(Z\) from color space XYZ. For color channel \(G\), average accuracy reached 82.5% (75% for fresh leaves and 90% for shade-dried leaves) for the J48 algorithm. For models built for selected textures from images in color channel \(R\), average accuracy ranged from 77.5% (KStar) to 90% (JRip). The higher accuracies of the classification of images of the dorsal side of fresh and shade-dried mint leaves were obtained for the models built based on textures selected separately for individual color channels of Lab space (Table 8). It indicated that the differences in the image textures were more noticeable. All applied machine learning algorithms provided 100% accuracy for textures from images in color channel \(a\). Slightly lower average accuracies reaching 97.5% (KStar, J48) and 95% (J48) were found in the case of the color channels \(b\) and \(L\), respectively.