DATASET: https://www.muratkoklu.com/datasets/

KOKLU Murat (a), UNLERSEN M. Fahri (b), OZKAN Ilker Ali (a), ASLAN M. Fatih(c), SABANCI Kadir (c)

(a) Department of Computer Engineering, Selcuk University, Turkey, Konya, Turkey
(b) Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Turkey
(c) Department of Electrical-Electronic Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

Citation Request :
Koklu, M., Unlersen, M. F., Ozkan, I. A., Aslan, M. F., & Sabanci, K. (2022). A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement, 188, 110425. Doi:https://doi.org/10.1016/j.measurement.2021.110425

Link: https://doi.org/10.1016/j.measurement.2021.110425

DATASET: https://www.muratkoklu.com/datasets/

Highlights
• Classification of five classes of grapevine leaves by MobileNetv2 CNN Model.
• Classification of features using SVMs with different kernel functions.
• Implementing a feature selection algorithm for high classification percentage.
• Classification with highest accuracy using CNN-SVM Cubic model.

Abstract: 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.

Keywords: Deep learningTransfer learningSVMGrapevine leavesLeaf identification
