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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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Classification of Turkish hazelnut (Corylus colurna L.) varieties: a comparative study of YOLOv8 and fine-tuned vision transformer
Scopus
Havuzumuzda
Hazelnut is a shrubby plant well-suited to the temperate and humid climate of the Black Sea region, particularly known for its mild winters. Turkey plays a critical role in global hazelnut production. Hazelnuts are not only an essential agricultural product for the country’s economy but are also highly valued for their nutritional content, being particularly rich in healthy oils and proteins, and their associated health benefits. The classification of different hazelnut species using modern deep learning techniques aimed to automatically distinguish between eight commonly cultivated hazelnut types: caklidak, damat, devedisi, sivri, karafindik, palaz, tombul, and yagli. This study employed both YOLOv8 classification models and Vision Transformer (ViT) with fine-tuning to classify a dataset comprising 2,722 labeled images of these hazelnut types. Various configurations of YOLOv8 models were tested, specifically YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls, alongside ViT-Base/16 with systematic fine-tuning optimization. The results demonstrate that the fine-tuned Vision Transformer achieved the highest classification accuracy of 99.75%, establishing a new state-of-the-art performance for this dataset. Among YOLOv8 models, YOLOv8s-cls and YOLOv8l-cls achieved 99.25% accuracy, while the lightest model, YOLOv8n-cls, recorded 97.38% accuracy. The superior performance of ViT-Base/16 (99.75%) represents a significant advancement over previous studies, demonstrating the effectiveness of transformer-based architectures with transfer learning for agricultural image classification. These findings highlight the strong potential for reliable use in real-world agricultural applications, with the Vision Transformer providing near-perfect classification accuracy. This study shows that advanced computer vision, particularly transformer-based models with fine-tuning, can effectively automate the identification and quality control of agricultural products, enhancing efficiency in food processing and supply chains.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Selçuk Üniversitesi
Selçuklu, Turkey
Sinop Üniversitesi
Sinop, Turkey