Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
A CNN-SVM study based on selected deep features for grapevine leaves classification
Scopus
Toplam 166 atıf DOI
The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this study, deep learning-based classification is conducted by using images of grapevine leaves. For this purpose, images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods. The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2. As the second approach, features were extracted from pre-trained MobileNetv2′s Logits layer and classification was made using various SVM kernels. As the third approach, 1000 features extracted from MobileNetv2′s Logits layer were selected by the Chi-Squares method and reduced to 250. Then, classification was made with various SVM kernels using the selected features. The most successful method was obtained by extracting features from the Logits layer and reducing the feature with the Chi-Squares method. The most successful SVM kernel was Cubic. The classification success of the system has been determined as 97.60%. It was observed that feature selection increased the classification success although the number of features used in classification decreased.
Atıf Kaynağı
Atıf Yapan Yayın
Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection
Scopus
Havuzumuzda 49 atıf almış
Coffee is an important export product of the tropical countries where it is grown. Therefore, the separation of coffee beans in the world in terms of the quality element and variety forgery is an important situation. Currently, the use of manual control methods leads to the fact that the parsing processes are inconsistent, time-consuming, and subjective. Automated systems are needed to eliminate such negative situations. The aim of this study is to classify 3 different coffee beans by using their images, through the transfer learning method by utilizing 4 different Convolutional Neural Networks-based models, which are SqueezeNet, Inception V3, VGG16, and VGG19. The dataset used in the models’ training was created specially for this study. A total of 1554 coffee bean images of Espresso, Kenya, and Starbucks Pike Place coffee types were collected with the created mechanism. Model training and model testing processes were carried out with the obtained images. In order to test the models, the cross-validation method was used. Classification success, Precision, Recall, and F-1 Score metrics were used for the detailed analysis of the models of performances. ROC curves were used for analyzing their distinctiveness. As a result of the tests, the average classification success of the models was determined as 87.3% for SqueezeNet, 81.4% for Inception V3, 78.2% for VGG16, and 72.5% for VGG19. These results demonstrate that the SqueezeNet is the most successful model. It is thought that this study may contribute to the subject of coffee beans of separation in the industry.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Amasya Üniversitesi
Amasya, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey