###### Abstract
The leaf features are easy to extract and analyze and can be used for the identification of plants. Manual identification can be vulnerable to human error. Therefore, automated system identification systems are desirable. In the case of mint leaf classification, features from the digital images of the front and back sides of leaves can be taken into account [14]. Intelligent computer models can be very effective in identifying herbs and their properties [15]. In the available literature, there is a lack of information on the presence of classification models based on textures of mint leaf images in the individual color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(b\), \(X\), \(Y\), and \(Z\), computed using image analysis techniques to monitor the effect of natural drying techniques on the changes in the structure of mint leaves. The novelty of the study is to reveal that the changes that occur after leaf drying can be distinguished with computer vision and machine learning applications. This study was aimed at comparing the effect of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of mint leaves using image processing and machine learning. The reason for undertaking the research was a need for a more complete understanding of the changes occurring in the image textures of mint leaves caused by different drying techniques to acquire new knowledge and develop the procedure for the detection of these changes. The contribution of the article is far superior to the current state of the art. The innovative nature of this study is related to the acquisition of new information, not found in the literature, on 1629 texture parameters of fresh and dried mint leaves and the selection of attributes with the highest discriminative power. The article presents a new approach to assessing changes in mint leaves caused by natural drying techniques. The leaf classification performed using models based on selected texture parameters extracted from the digital color images converted to color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(b\), \(X\), \(Y\), and \(Z\) built using machine learning algorithms from groups of Lazy, Rules, and Trees is a unique approach to distinguish dried and fresh mint leaves. For the first time, the assessment of the influence of natural drying techniques on the structure of mint leaves was carried out using attributes selected from sets of 1629 image textures from different color channels. Furthermore, monitoring the degree of changes in the structure of leaves under the influence of drying without the need to damage or destroy the leaves can be a problem. The proposed approach may be a non-destructive, objective, cost-effective, and fast practical solution to this problem. Justification for tackling the problem results from insufficient data in the literature on an experimental and theoretical description of the changes in texture parameters of mint leaf images as a result of the drying. The problem addressed in this study is to reveal, with technological solutions, the textural changes caused by the drying techniques used to preserve the mint leaf for a long time. The proposed solution is to combine the power of computer vision and machine learning, as used in many different agricultural applications today [16; 17]. In this way, discrimination systems with non-destructive, non-biased, high-accuracy, and autonomous capabilities can be developed. The proposed solution also does not depend on a single machine learning algorithm or a single color space of images. The robustness of the proposed approach was proven with different machine learning methods and image textures from different color spaces determined experimentally.