## 4 Discussion  
In a task to recognize fruits and vegetables by blending color, shape, and texture features, ref. [39] used classifiers such as KNN, linear discriminant analysis, Naive Bayes, error-correcting output classifier, and decision tree classifier. A 10 cross-validation technique is used, and KNN achieved a classification accuracy of 97.5% with 2400 images from 24 categories of fruits and vegetables. Likewise, in an attempt to classify six apple varieties,  
Figure 8: KNN confusion matrix. Figure 7: MLP confusion matrix. [PERSON] et al. [40] applied a bag of visual words (BoVW) and reported an accuracy of 96% using polynomial SVM. In the development of an automatic apple grading model, which classifies apples in real-time based on physical parameters of apples such as size, color, and external defects, ref. [41], reported that the ANN sorter achieved an accuracy of 96% during overall grading. In a task of automatic apple sorting system using machine vision, Golden and Starking Delicious, and Granny Smith apple cultivars were sorted into different classes by their color, size, and weight, and the system achieved a sorting accuracy rate of 73-96% using decision tree [42]. In their study to classify six apple varieties [15], researchers achieved the highest accuracy of 95.27% using statistical, textural, geometrical, discrete wavelet transform, HOG, and Laws' energy texture features with an SVM classifier. In related studies, ref. [21] used shape-based features to classify seven fruit types and achieved 95% accuracy using the Naive Bayes classifier. Similarly, in another study were utilized two feature descriptor methods to classify 15 fruit types with a maximum accuracy of 97% using MLP and KNN classifiers [22]. [PERSON] et al. [23] compared the performance of six supervised machine-learning techniques to classify fruit types based on visual features. They combined Hue, Color-SIFT, Discrete Wavelet Transform, and Haralick features and reported classification accuracies between 99% and 100% using back propagation neural network, SVM, and KNN classifiers. In their study, ref. [24] employed color and texture features to classify ten fruit types using an SVM classifier. Researchers in [24] utilized HSV color space for thresholding and region of interest extraction and extracted color features through hue and saturation. Texture features were obtained by subjecting the luminance channel to three level discrete wavelet transform. Their results showed that the SVM classifier achieved an accuracy of 95.3%. Moreover, ref. [19] classified six fruit types: apple, banana, orange, pear, watermelon, and mango. Using thresholding and morphological processing, they extracted features such as color, area, centroid, zone, perimeter, size, and roundness, and out of five algorithms SVM achieved the highest accuracy of 91.67%. Similarly, [20] used color and texture features to identify fruit types. They employed a KNN classifier, which achieved an accuracy of 96% to identify seasonal fruits and detect rotten fruit among fresh ones. For detecting rotten fruit, they used the SVM classifier, which achieved a performance of 98%. A comparison showing the performance results of our proposed method and previous studies discussed in the above paragraphs has been provided in Table 3.  
In our study, within the broader context of related studies, it can be inferred that the effectiveness of the strategy applied, and the results, are promising with the application of combined color moment and HOG features and a 10 Skfold technique. Comparatively, our approach outperformed related results, likely due to the incorporation of color moments