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

穆拉特·科克吕（KOKLU Murat）(a)、M·法赫里·于勒尔森（UNLERSEN M. Fahri）(b)、伊尔克尔·阿里·厄兹坎（OZKAN Ilker Ali）(a)、M·法提赫·阿斯兰（ASLAN M. Fatih）(c)、卡迪尔·萨班哲（SABANCI Kadir）(c)
(a) 土耳其科尼亚市塞尔丘克大学（Selcuk University）计算机工程系
(b) 土耳其科尼亚市内克梅丁·埃尔巴坎大学（Necmettin Erbakan University）电气与电子工程系
(c) 土耳其卡拉曼市卡拉曼奥卢·穆罕默德贝伊大学（Karamanoglu Mehmetbey University）电气电子工程系
数据集链接：https://www.muratkoklu.com/datasets/
引文要求：
科克吕, M., 于勒尔森, M. F., 厄兹坎, I. A., 阿斯兰, M. F., & 萨班哲, K. (2022). 基于精选深度特征的CNN-SVM模型用于葡萄叶片分类研究[J]. 《测量》（Measurement）, 188, 110425. DOI: https://doi.org/10.1016/j.measurement.2021.110425
原文链接：https://doi.org/10.1016/j.measurement.2021.110425
数据集链接：https://www.muratkoklu.com/datasets/
研究亮点
• 采用MobileNetv2卷积神经网络（Convolutional Neural Network, CNN）模型对5类葡萄叶片开展分类
• 采用支持向量机（Support Vector Machine, SVM）结合不同核函数完成特征分类
• 引入特征选择算法以提升分类准确率
• 采用CNN-SVM三次核模型实现最高精度的分类
摘要：葡萄的主要经济产物为鲜食或加工用葡萄。此外，葡萄叶片作为副产物每年可采收一次。葡萄叶片的品种对其价格与风味至关重要。本研究基于葡萄叶片图像开展深度学习（Deep Learning）分类任务。为此，研究团队采用专用自照明采集系统，采集了隶属于5个葡萄品种的500张叶片图像，随后通过数据增强（Data Augmentation）技术将样本总量扩充至2500张。
首先，采用经微调的前沿卷积神经网络（CNN）模型MobileNetv2完成分类任务。其次，从预训练MobileNetv2模型的Logits层提取特征，结合多种SVM核函数开展分类。第三，从MobileNetv2模型的Logits层提取的1000个特征中，通过卡方检验（Chi-Squares）法筛选出250个特征，随后利用筛选后的特征结合多种SVM核函数完成分类。
实验结果显示，从Logits层提取特征并通过卡方检验法降维的方案为最优方案，其中表现最佳的SVM核函数为三次核（Cubic）。本系统的分类准确率可达97.60%，且研究发现尽管分类所用的特征数量减少，特征选择仍可有效提升分类性能。
关键词：深度学习（Deep Learning）、迁移学习（Transfer Learning）、支持向量机（SVM）、葡萄叶片、叶片识别

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。