Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A CNN-SVM study based on selected deep features for grapevine leaves classification
Measurement Journal of the International Measurement Confederation · Ocak 2022
YÖKSİS Kayıtları
A CNN-SVM Study Based on Selected Deep Features for Grapevine Leaves Classification
Measurement · 2022 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
A CNN-SVM study based on selected deep features for grapevine leaves classification
Measurement · 2022 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods
2016 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering
2016 ISSN: 02632241 SCI-Expanded
Prof. Dr. HASAN ERDİNÇ KOÇER →
Point cloud filtering on UAV based point cloud
2019 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA ZEYBEK →
Investigation of progressive tool wear for determining of optimized machining parameters in turning
2019 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
Analysis of effect factors on thermoelectric generator using Taguchi method
2020 ISSN: 0263-2241 SCI
Dr. Öğr. Üyesi HAKAN TERZİOĞLU →
Decomposition of process damping ratios and verification of process damping model for chatter vibration
2012 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis
2012 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Optimization of tool geometry parameters for turning operations based on the response surface methodology
2011 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
A new process damping model for chatter vibration
2011 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
2021 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
The determination of age and gender by implementing new image processing methods and measurements to dental X-ray images
2020 ISSN: 0263-2241 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey
2021 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA ZEYBEK →
Measuring curvature of trajectory traced by coupler of an optimal four-link spherical mechanism
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi OSMAN ACAR →
A CNN-SVM Study Based on Selected Deep Features for Grapevine Leaves Classification
2022 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
An S-band zero-IF SFCW through-the-wall radar for range, respiration rate, and DOA estimation
2021 ISSN: 0263-2241 SCI-Expanded Q1
Prof. Dr. İSMAİL SARITAŞ →
An experimental study: Detecting the respiration rates of multiple stationary human targets by stepped frequency continuous wave radar
2021 ISSN: 0263-2241 SCI-Expanded Q1
Prof. Dr. İSMAİL SARITAŞ →
An experimental study: Detecting the respiration rates of multiple stationary human targets by stepped frequency continuous wave radar
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
An S-band zero-IF SFCW through-the-wall radar for range, respiration rate, and DOA estimation
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
A CNN-SVM study based on selected deep features for grapevine leaves classification
2022 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →
Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends
2022 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
Makale Bilgileri
ISSN02632241
Yayın TarihiOcak 2022
Cilt / Sayfa188
Scopus ID2-s2.0-85119057932
Özet
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.
Yazarlar (5)
1
Murat Koklu
ORCID: 0000-0002-2737-2360
2
M. Fahri Unlersen
3
Ilker Ali Ozkan
4
M. Fatih Aslan
5
Kadir Sabanci
Anahtar Kelimeler
Deep learning
Grapevine leaves
Leaf identification
SVM
Transfer learning
Kurumlar
Karamanoğlu Mehmetbey Üniversitesi
Karaman Turkey
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey
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Scimago Dergi (ISSN Eşleşmesi)
Measurement: Journal of the International Measurement Confederation
Q1
SJR Skoru1,244
H-Index146
YayıncıElsevier B.V.
ÜlkeNetherlands
Applied Mathematics (Q1)
Condensed Matter Physics (Q1)
Education (Q1)
Electrical and Electronic Engineering (Q1)
Instrumentation (Q1)
Statistics and Probability (Q1)
Metrikler
159
Atıf
5
Yazar
5
Anahtar Kelime