## 2 Materials and Methods
### Classification of Fresh, Shade and Open-Air Sun-dried Mint Leaves  
The classification of leaf samples was performed using WEKA machine learning software (Machine Learning Group, University of Waikato) [21; 22; 23]. The influence of two drying techniques on the ventral and dorsal sides of mint leaves was compared. Models based on selected image features for individual color channels were built for distinguishing fresh vs. shade-dried mint leaves on the ventral side, fresh vs. open-air sun-dried mint leaves on the ventral side, fresh vs. shade-dried mint leaves on the dorsal side, and fresh vs. open-air sun-dried mint leaves on the dorsal side. First, the attribute (texture) selection was performed using the Best First algorithm with the Correlation-based Feature Selection (CFS) subset evaluator [24; 25]. The attributes were selected separately from the datasets including texture parameters of images in each of color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(b\), \(X\), \(Y\), and \(Z\) belonging to color spaces RGB, Lab, and XYZ. The classification analysis was performed using a test mode of 10-fold cross-validation. Various machine learning algorithms such as IBk, KStar and LWL from the group of Lazy, JRip and PART from Rules, Bayes Net and Naive Bayes from Bayes, Logistic, Multilayer Perceptron and RBF Classifier from Functions, Random Forest, J48 and LMT from Trees, and Filtered Classifier, Multi Class Classifier and Logit Boost from Meta were tested.  
The methods chosen for classification are frequently used for machine learning. Any machine learning method used produces successful results if the extracted features strongly represent the target data. The physical changes caused by drying directly affect the pixel distribution in the image. For this, the texture-based extracted features are strong aids for distinguishing the drying method. In different spaces, the texture changes are also different. Some spaces show changes more clearly. For example, the green channel of the image is used in studies involving the segmentation of the retinal blood vessel, because in this channel the blood vessel is more vivid [26]. Therefore, examining the changes in texture on different channels may provide a more accurate distinction. In addition to the powerful features, the chosen classification algorithm also has a significant effect on success. Although strong features generally provide successful results, different learning methods provide different accuracy due to their methodology. In this context, extracting texture features from different channels and obtaining results with different classification algorithms makes the choice of method in this study powerful. The methodological steps of the study are shown in Figure 5. After all these steps, different result metrics and indicators showing the performance of the study are shared in the result section to show that all the experiments were done correctly. As a result, the confusion matrices with accuracies for each class, the average accuracies and values of Precision, Recall, F-Measure, and MCC (Matthews Correlation Coefficient) were determined [27; 28; 29; 30].  
Figure 4: The images of the dorsal side (lower surface) of fresh and dried mint leaves in different color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(X\), and \(Y\).