## 3 Results
#### 3.2.2 Classification of the Images of the Dorsal Side of Fresh and Open-Air Sun-dried Mint Leaves
Furthermore, an average accuracy of 97.5% was found for models built using KStar and J48 based on selected textures of images in color channel \(Y\). In the case of individual color channels from color space XYZ, the lowest correctness of distinguishing fresh and open-air sun-dried mint leaves based on image textures of the dorsal leaf side was determined for color channel \(X\). Average accuracy reached 95% for a model developed using J48. Texture features from images in color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(b\), \(X\), \(Y\), and \(Z\) and artificial intelligence involving models built by traditional machine learning algorithms proved to be useful for distinguishing fresh and shade drying or open-air sun-dried mint leaves. Machine learning-based algorithms are considered an innovative approach to advancing food drying technology. Machine learning algorithms have a superior capacity to predict specific trends and patterns using large volumes of data. Machine learning-based strategies are used, among others, for drying modeling to determine the optimal drying condition to improve product quality and reduce energy consumption. Machine learning models \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**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**} \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multicolumn{8}{c}{Color channel \(X\)} \\ lazy:KStar & 95 & 5 & Fresh & 92.5 & 0.905 & 0.950 & 0.927 & 0.851 \\ \multicolumn{8}{c}{} \\ rules.JRip & 10 & 90 & Open-air sun-dried & 0.947 & 0.900 & 0.923 & 0.851 \\ \multicolumn{8}{c}{} \\ rules.JRip & 95 & 5 & Fresh & 90 & 0.864 & 0.950 & 0.905 & 0.804 \\ \multicolumn{8}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 90 & 1.000 & 0.952 & 0.905 \\ \multicolumn{8}{c}{} \\  & 10 & 90 & Open-air sun-dried & 1.000 & 0.900 & 0.947 & 0.905 \\ \multicolumn{8}{c}{} \\  & 100 & 0 & Fresh & 952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{8}{c}{} \\ lazy:KStar & 5 & 95 & Open-air sun-dried & 97.5 & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{8}{c}{} \\ rules.JRip & 90 & 10 & Fresh & 947 & 0.900 & 0.923 & 0.851 \\ \multicolumn{8}{c}{} \\ rules.JRip & 5 & 95 & Open-air sun-dried & 92.5 & 0.905 & 0.950 & 0.927 & 0.851 \\ \multicolumn{8}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{8}{c}{} \\  & 5 & 95 & Open-air sun-dried & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{8}{c}{} \\  & 100 & 0 & Fresh & 952 & 1.000 & 0.976 & 0.951 \\ \multicolumn{8}{c}{} \\ rules.JRip & 5 & 95 & Open-air sun-dried & 1.000 & 0.950 & 0.974 & 0.951 \\ \multicolumn{8}{c}{} \\ trees.J48 & 100 & 0 & Fresh & 1.000 & 1.000 & 1.000 & 1.000 \\ \multicolumn{8}{c}{} \\  & 0 & 100 & Open-air sun-dried & 1.000 & 1.000 & 1.000 & 1.000 \\ \multicolumn{8}{c}{} \\ \end{tabular}
\end{table} 
Table 12: The performance metrics of distinguishing fresh and open-air sun-dried mint leaves using textures from images of the dorsal leaf side in color channels \(X\), \(Y\), and \(Z\) from color space XYZ.