A New Method Based on Extracting, Combining and Selecting Deep Features from Natural and Synthetic Data for Classification of Grapevine Leaf Species

In recent years, the production and consumption of grapevine leaves containing rich minerals, dietary fiber and vitamins has been intensively realized in Turkey. However, the demand for stuffed food made from grapevine leaves in the convenience food sector increases the export opportunities to different countries In commercial agricultural activities such as this, it is important to determine quality standards for sustainable marketing. Researchers have made positive progress with deep learning in smart farming applications. In this study, we propose a new method for recognizing the type of grapevine leaves to be used for consumption. In the proposed method, a dataset containing 500 images of 5 different grapevine leaf species, namely Ak, Ala Idris, Büzgülü, Dimnit and Nazli, was used. From these images, 3500 images were obtained with data augmentation techniques. In addition, a dataset consisting of more detailed textures was obtained by applying the ESRGAN model to the obtained images. VGG 19 deep learning model was used to extract features from these images. The features obtained from the two separate data sets were combined. In this way, a hybrid feature extraction model was created. The best 175 feature subsets were selected using the PCA algorithm. Finally, 96.14% accuracy was calculated using a Support Vector Machine (SVM) for classification of the obtained features.