###### Abstract  
The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels \(R\), \(G\), \(B\), \(L\), \(a\), \(b\), \(X\), \(Y\), and \(Z\). Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels \(B\) (KStar and J48 machine learning algorithms), \(a\) (KStar and J48), \(b\) (KStar), and \(Y\) (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels \(R\) and \(G\) (J48), \(B\), \(a\) and \(b\) (KStar, Rjp, and J48), and \(X\) and \(Y\) (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel \(B\) (KStar) and \(a\) (KStar, JRjp, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels \(a\) (KStar, JRip, J48), \(b\) (J48), and \(Z\) (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves. During open-air sun drying, plants are in an open environment directly exposed to solar radiation. This open exposure also results in direct exposure to contamination with insects, dust, or sand particles [5]. Furthermore, it causes discoloration and loss of aroma, which is important for consumers because it can reduce dehydrated-mint quality and quantity. Thermal processing results in numerous reactions affecting the hue. The Maillard reaction, degradation of chlorophyll pigment, and oxidation of ascorbic acid take place. Open-air sun drying causes browning. Furthermore, it destroys the vitamin C content, which is heat- and light-sensitive [3]. Another herb drying method that employs solar energy as a heating source is shade drying. This drying approach protects photosensitive compounds while reducing light-induced chemical reactions such as oxidation [6]. However, the drying time of shade drying is longer than that of sun drying [7]. Additionally, shade drying is a better method for drying herbs since it maintains the integrity of the trichomes [8]. Shade drying also causes less damage to the epidermal surface than sun drying [9]. Nonetheless, due to its low investment cost and high-quality dried products, shade drying, like sun drying, is still popular in rural areas and small enterprises [10]. The leaf classification can be performed based on color, shape, and texture parameters using computer vision [11]. Image processing can be used to detect leaves and extract leaf parameters useful for leaf classification using machine learning algorithms [12]. Machine learning techniques for developing Artificial Intelligence allow computers or machines to learn from experience without explicit programming. The objective of machine learning algorithms is to improve the performance of models using data [13]. Thanks to computer vision-based methods for classifying leaves, various and powerful features can be extracted from leaf images.