The main ingredient of flour is processed wheat. Wheat is an agricultural product that is harvested once a year. It may be necessary to choose the variety of wheat for growing wheat and efficient harvesting. The variety of wheat is important for its economic value, taste, and crop yield. Although there are many varieties of wheat, they are very similar in colour, size, and shape, and it requires expertise to distinguish them by eye. This is very time consuming and can lead to human error. Using computer vision and artificial intelligence, such problems can be solved more quickly and objectively. In this study, an attempt was made to classify five bread wheat varieties belonging to different cultivars using Convolutional Neural Network (CNN) models. Three approaches have been proposed for classification. First, pre-trained CNN models (ResNet18, ResNet50, and ResNet101) were trained for bread wheat cultivars. Second, the features extracted from the fc1000 layer of the pre-trained CNN models ResNet18, ResNet50, and ResNet101 were classified using a support vector machine (SVM) classifier with different kernel features from machine learning techniques for classification with different variants. Finally, SVM methods were used in the second stage to classify the features obtained from the fc1000 layer of the pre-trained CNN models with an optimal set of features that can represent all features using the minimum redundancy maximum relevance (mRMR) feature selection algorithm.The accuracies obtained in the first, second, and last phases are as follows. In the first phase, the most successful method in classifying wheat grains was the ResNet18 model with 97.57%. In the second phase, the ResNet18 + ResNet50 + ResNet101 + Quadratic SVM model was the most successful model in classification using the features obtained from the ResNet CNN models with 94.08%.The accuracy for classification with the 1000 most effective features selected by the feature selection algorithm was 94.51%. Although the classification with features is slightly lower than deep learning, the classification time is much shorter and is 93%. This result confirms the great effectiveness of CNN models for wheat grain classification.