Adige, SevimKurban, RifatDurmus, AliKarakose, Ercan2025-09-252025-09-2520230941-06431433-3058https://doi.org/10.1007/s00521-023-08340-3https://hdl.handle.net/20.500.12573/3457Karakose, Ercan/0000-0001-5586-3258; Kurban, Rifat/0000-0002-0277-2210; Durmus, Ali/0000-0001-8283-8496One of the most important problems for farmers who produce large amounts of apples is the classification of the apples according to their types in a short time without handling them. Support vector machines (SVM) and deep residual networks (ResNet-50) are machine learning methods that are able to solve general classification situations. In this study, the classification of apple varieties according to their genus is made using machine learning algorithms. A database is created by capturing 120 images from six different apple species. Bag of visual words (BoVW) treat image features as words representing a sparse vector of occurrences over the vocabulary. BoVW features are classified using SVM. On the other hand, ResNet-50 is a convolutional neural network that is 50 layers deep with embedded feature extraction layers. The pre-trained ResNet-50 architecture is retrained for apple classification using transfer learning. In the experiments, our dataset is divided into three cases: Case 1: 40% train, 60% test; Case 2: 60% train, 40% test; and Case 3: 80% train, 20% test. As a result, the linear, Gaussian, and polynomial kernel functions used in the BoVW + SVM algorithm achieved 88%, 92%, and 96% accuracy in Case 3, respectively. In the ResNet-50 classification, the root-mean-square propagation (rmsprop), adaptive moment estimation (adam), and stochastic gradient descent with momentum (sgdm) training algorithms achieved 86%, 89%, and 90% accuracy, respectively, in the set of Case 3.eninfo:eu-repo/semantics/closedAccessSupport Vector MachinesDeep Residual NetworksApple ClassificationArticle10.1007/s00521-023-08340-32-s2.0-85148440988