Grapevine Leaves Image Dataset

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
DATASET: https://www.muratkoklu.com/datasets/
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 learning, Transfer learning, SVM, Grapevine leaves, Leaf identification

搜集汇总

背景与挑战

背景概述

该数据集包含5个品种的葡萄叶图像，原始图像500张，经数据增强后扩展至2500张，用于基于MobileNetv2 CNN模型和SVM的叶片分类研究，最高分类准确率达97.60%。数据集适用于人工智能、计算机视觉和图像处理等领域的研究。