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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.
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Deep learning and evolutionary intelligence with fusion-based feature extraction for classification of wheat varieties
Scopus
Havuzumuzda Open Access 7 atıf almış
One of the most important aspects of producing quality wheat is obtaining pure wheat seed varieties. It is of great importance to obtain pure wheat seeds for high grain quality, efficiency, and durability of wheat varieties. For this purpose, collective wheat images of 5 different bread wheat seed varieties registered by computer vision system were taken. Then, 8354 bread wheat grain images were obtained using image processing techniques. The use of important features that affect the image classification is critical for high classification success. The features obtained from CNN models are fused and combined. The optimal feature subset was selected with the whale optimization algorithm (WOA), one of the meta-heuristic algorithms. Each resulting feature set is classified by machine learning algorithms. The best performance in classification results was obtained with the Support Vector Machine (SVM) classifier. The performance of the system was 95.2% with Fusion + SVM and WOA + SVM. The study also provides results of performance metrics such as sensitivity, precision, specificity and F1 score, Matthews correlation coefficient and kappa values. The contribution of the article is as follows the use of the proposed method allows this process to be carried out with fewer features, less time, and less cost, as well as high accuracy in the classification of bread wheat seed varieties.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey