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    Convolutional Neural Networks Based Hybrid Deep Model for Grapevine Leaves Detection and Classification
    (Int Information & Engineering Technology Assoc, 2025) Atesoglu, Fatih; Bingol, Harun
    The problem of determining the type of grapevine leave (GL) has an important place in the agricultural field and especially in the field of viticulture. It is a foodstuff that is consumed as a table especially in every Middle Eastern country and its export to Europe has been increasing in recent years. GL are usually consumed as wraps. Considering the economic situation of such a widely used plant, the determination of the plant type is very important. Because early intervention is required for diseases that will occur in the plant. As it is known, early diagnosis will facilitate treatment. In this study, leaf types were classified using artificial intelligence techniques in order to help experts diagnose the type of vine leaf. In addition, a hybrid model was proposed for classification processes. In this proposed hybrid model, feature maps were first extracted from the Resnet50 and Inceptionv3 deep models and these features were combined. AThen, the most valuable features in the feature map were selected with the Neighborhood Component Analysis (NCA) method. Then, the feature map containing the most valuable features was classified in the best-known supervised classification methods. The proposed hybrid model reached an accuracy value of 89.2%. Thus, it has been determined that the proposed hybrid model achieves a highly competitive accuracy value in the classification of vine leaf images and can be used for this purpose.
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    The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI
    (Mdpi, 2025) Atesoglu, Fatih; Bingol, Harun
    There are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases.