## 1 Introduction
[17] also developed a shallow convolutional neural network (CNN) for apple species classification and concluded that the model presented an alternative in classification-related tasks and brings attention to reducing the complexity of deep neural networks. The study highlights the importance of feature selection and classifier choice in image classification tasks. [PERSON] et al. [18] studied the recognition of 15 different kinds of fruits using surface and geometric features and showed that by combining all features, namely color, texture,  
Figure 1: Average apple production for the year (1994–2021). size, and shape, the overall recognition rate for all classifiers increased and reported that KNN outperformed the other classifiers. In their study on fruit classification, [PERSON] and [PERSON] [19] examined six types of fruit including apple, banana, orange, pear, watermelon, and mango. They used thresholding and morphological processing to identify the area of interest and extract features such as color, area, centroid, zone, perimeter, size, and roundness and they employed five different machine learning algorithms: KNN, SVM, Naive Bayes, random forest, and neural network to classify the fruits. To identify the type of seasonal fruits and detect spoiled ones, ref. [20] trained KNN and SVM algorithms using color and texture features and distinguished four types of fruit and detected spoiled fruit among fresh ones. A similar study was conducted on fruit recognition and classification using shape-based features for auto-harvesting [21]. They classified seven fruit types based on features such as fruit area, perimeter, major and minor axis lengths, and distance between the foci of an equivalent ellipse by employing the Naive Bayes classifier. In their study, [PERSON] et al. [22] utilized two feature descriptor methods to classify 15 different fruit types, using images with white backgrounds obtained from the internet. The zoning and character edge descriptor features were combined with discrete Fourier transform to extract the features. MLP and KNN classifiers were adopted. In their work to analyze visual features and classifiers to classify fruit types, [PERSON] et al. [23] investigated the combination of hue, color-SIFT, discrete wavelet transform, and Haralick features by comparing the results obtained from six supervised machine learning techniques including KNN, SVM, Naive Bayes, linear discriminant analysis, decision trees, and feed forward back propagation neural network. Applying statistical features to classify ten fruit types, other researchers utilized color and texture features [24]. In the feature extraction processes, they used HSV color space to perform thresholding and extract the region of interest, extracting color features through hue and saturation. The luminance channel was subjected to a three-level discrete wavelet transform to obtain texture features. Therefore, our study aims to develop an efficient and repeatable system for classifying ten apple varieties by extracting textural and color features from RGB apple images. Developing a robust ML model depends on the application of representative features, the right combination of extracted features whenever different types of parameters are involved, and dimensionality reduction in the presence of numerous features. Thus, this study investigated the effect of combining textural (HOG) and color features (color moments) and compared the performances of widely used ML algorithms to find the best-performing classifier for apple varieties classification. Overall, combining HOG and color moments is what makes this study different from the aforementioned studies and its contribution is to investigate the effect of feature fusion in enhancing the classification accuracy of ML algorithms to classify apple varieties.