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
Analysis of selected deep features with CNN-SVM-based for bread wheat seed classification
Scopus
Havuzumuzda Open Access 16 atıf almış
The main ingredient of flour is processed wheat. Wheat is an agricultural product that is harvested once a year. It may be necessary to choose the variety of wheat for growing wheat and efficient harvesting. The variety of wheat is important for its economic value, taste, and crop yield. Although there are many varieties of wheat, they are very similar in colour, size, and shape, and it requires expertise to distinguish them by eye. This is very time consuming and can lead to human error. Using computer vision and artificial intelligence, such problems can be solved more quickly and objectively. In this study, an attempt was made to classify five bread wheat varieties belonging to different cultivars using Convolutional Neural Network (CNN) models. Three approaches have been proposed for classification. First, pre-trained CNN models (ResNet18, ResNet50, and ResNet101) were trained for bread wheat cultivars. Second, the features extracted from the fc1000 layer of the pre-trained CNN models ResNet18, ResNet50, and ResNet101 were classified using a support vector machine (SVM) classifier with different kernel features from machine learning techniques for classification with different variants. Finally, SVM methods were used in the second stage to classify the features obtained from the fc1000 layer of the pre-trained CNN models with an optimal set of features that can represent all features using the minimum redundancy maximum relevance (mRMR) feature selection algorithm.The accuracies obtained in the first, second, and last phases are as follows. In the first phase, the most successful method in classifying wheat grains was the ResNet18 model with 97.57%. In the second phase, the ResNet18 + ResNet50 + ResNet101 + Quadratic SVM model was the most successful model in classification using the features obtained from the ResNet CNN models with 94.08%.The accuracy for classification with the 1000 most effective features selected by the feature selection algorithm was 94.51%. Although the classification with features is slightly lower than deep learning, the classification time is much shorter and is 93%. This result confirms the great effectiveness of CNN models for wheat grain classification.
Atıf Yapan Makale Bilgileri
Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey