Classification of apple images using support vector machines and deep residual networks
Özet
One of the most important problems for farmers who produce large amounts of apples is the classification of the apples
accordingtotheir typesinashorttimewithouthandlingthem. Supportvectormachines(SVM) anddeepresidualnetworks
(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 pretrained
ResNet-50 architecture is retrained for apple classification using transfer learning. In the experiments, ourdataset 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.
