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A CNN-SVM study based on selected deep features for grapevine leaves classification
Scopus
Toplam 167 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.
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Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
Scopus
Havuzumuzda 60 atıf almış
Since dry bean varieties have different qualities and economic values, their separation is of great importance in the field of agriculture. In recent years, the use of artificial intelligence-supported and image-based systems has become widespread for this process. This study aims to create a data set consisting of 14 classes in the detection of dry beans and to investigate the effectiveness of the hybrid structure of the extreme learning machine (ELM) model with GoogLeNet transfer learning on this dataset. At the same time, the salp swarm algorithm (SSA), which is one of the swarm intelligence algorithms, was used to test its applicability in ELM classifier by optimizing ELM parameters. The performance of these models was compared with ELM-based particle swarm optimization, harris hawks optimization, artificial bee colony, and traditional machine learning algorithms such as support vector machine and k-nearest neighbor. The suggested SSA-ELM model successfully classifies 14 different types of dry beans with a success rate of 91.43%. The comparable results demonstrate that the proposed hybrid model had better classification accuracy and performance metrics than traditional machine learning algorithms. In addition, it is seen that the use of image data, extraction of deep features, and classification with optimized ELM in the classification of dry beans have achieved comparable success in the literature.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey