期刊
NEURAL COMPUTING & APPLICATIONS
卷 35, 期 16, 页码 12073-12087出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-023-08340-3
关键词
Support vector machines; Deep residual networks; Apple classification
This study uses machine learning algorithms for the classification of apple varieties, with support vector machines (SVM) and deep residual networks (ResNet-50) being the methods used. By creating a database and using image feature classification, the SVM algorithm achieved 88%, 92%, and 96% accuracy in Case 3, while the ResNet-50 algorithm achieved 86%, 89%, and 90% accuracy in Case 3.
One 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.
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