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Scopus YÖKSİS DOI Eşleşti SJR Q2

Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection

Food Analytical Methods · Aralık 2022

YÖKSİS DOI Eşleşmesi Bulundu

Bu Scopus makalesi YÖKSİS veritabanında da kayıtlı. Aşağıda YÖKSİS verilerini görebilirsiniz.

YÖKSİS Kayıtları
Application of Pre-Trained Deep Convolution Neural Networks for Coffee Beans Species Detection
Food Analytical Methods · 2022 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
Application of Pre-Trained Deep Convolution Neural Networks for Coffee Beans Species Detection
Food Analytical Methods · 2022 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Application of Pre-Trained Deep Convolution Neural Networks for Coffee Beans Species Detection
Food Analytical Methods · 2022 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Application of Pre-Trained Deep Convolution Neural Networks for Coffee Beans Species Detection
Food Analytical Methods · 2022 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 8 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Optimization of the Extraction Process of Antioxidants from Orange Using Response Surface Methodology
2015 ISSN: 1936-9751 SCI-Expanded
Prof. Dr. GÖKHAN ZENGİN →
Monitoring of Zn II Cd II Pb II and Cu II During Refining of Some Vegetable Oils Using Differential Pulse Anodic Stripping Voltammetry
2014 ISSN: 1936-9751 SCI-Expanded
Prof. Dr. HÜSEYİN KARA →
Tamarindus indica L. Seed: Optimization of Maceration Extraction Recovery of Tannins
2020 ISSN: 1936-9751 SCI
Prof. Dr. GÖKHAN ZENGİN →
Multivariate Modeling for Quantifying Adulteration of Sunflower Oil with Low Level of Safflower Oil Using ATR-FTIR, UV-Visible, and Fluorescence Spectroscopies: A Comparative Approach
2020 ISSN: 1936-9751 SCI-Expanded Q3
Doç. Dr. İSMAİL TARHAN →
Multivariate Modeling for Quantifying Adulteration of Sunflower Oil with Low Level of Safflower Oil Using ATR-FTIR, UV-Visible, and Fluorescence Spectroscopies: A Comparative Approach
2021 ISSN: 1936-9751 SCI-Expanded Q3
Arş. Gör. MUHAMMED RAŞİT BAKIR →
Application of Pre-Trained Deep Convolution Neural Networks for Coffee Beans Species Detection
2022 ISSN: 1936-9751 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Monitoring of Zn II Cd II Pb II and Cu II During Refining of Some Vegetable Oils Using Differential Pulse Anodic Stripping Voltammetry
2014 ISSN: 1936-9751 SSCI
Prof. Dr. SEMAHAT KÜÇÜKKOLBAŞI →
Multivariate Modeling for Quantifying Adulteration of Sunflower Oil with Low Level of Safflower Oil Using ATR-FTIR, UV-Visible, and Fluorescence Spectroscopies: A Comparative Approach
2020 ISSN: 1936-9751 SCI-Expanded Q2
Arş. Gör. MUHAMMED RAŞİT BAKIR →

Makale Bilgileri

Dergi Food Analytical Methods
ISSN19369751
Yayın TarihiAralık 2022
Cilt / Sayfa15 · 3232-3243
DOI10.1007/s12161-022-02362-8
Scopus ID2-s2.0-85135329785
Özet Coffee is an important export product of the tropical countries where it is grown. Therefore, the separation of coffee beans in the world in terms of the quality element and variety forgery is an important situation. Currently, the use of manual control methods leads to the fact that the parsing processes are inconsistent, time-consuming, and subjective. Automated systems are needed to eliminate such negative situations. The aim of this study is to classify 3 different coffee beans by using their images, through the transfer learning method by utilizing 4 different Convolutional Neural Networks-based models, which are SqueezeNet, Inception V3, VGG16, and VGG19. The dataset used in the models’ training was created specially for this study. A total of 1554 coffee bean images of Espresso, Kenya, and Starbucks Pike Place coffee types were collected with the created mechanism. Model training and model testing processes were carried out with the obtained images. In order to test the models, the cross-validation method was used. Classification success, Precision, Recall, and F-1 Score metrics were used for the detailed analysis of the models of performances. ROC curves were used for analyzing their distinctiveness. As a result of the tests, the average classification success of the models was determined as 87.3% for SqueezeNet, 81.4% for Inception V3, 78.2% for VGG16, and 72.5% for VGG19. These results demonstrate that the SqueezeNet is the most successful model. It is thought that this study may contribute to the subject of coffee beans of separation in the industry.

Yazarlar (5)

1
Yavuz Unal
ORCID: 0000-0002-3007-679X
2
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
3
Ilkay Cinar
ORCID: 0000-0003-0611-3316
4
Ramazan Kursun
ORCID: 0000-0002-6729-1055
5
Murat Koklu
ORCID: 0000-0002-2737-2360

Anahtar Kelimeler

CNN Coffee beans Deep learning Transfer learning

Kurumlar

Amasya Üniversitesi
Amasya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Food Analytical Methods
Q2
SJR Skoru0,541
H-Index72
YayıncıSpringer
ÜlkeUnited States
Analytical Chemistry (Q2)
Food Science (Q2)
Safety Research (Q2)
Safety, Risk, Reliability and Quality (Q2)
Applied Microbiology and Biotechnology (Q3)
Dergi sayfasına git

Metrikler

47
Atıf
5
Yazar
4
Anahtar Kelime

Sistemimizdeki Yazarlar

RK
RAMAZAN KURŞUN
Öğr. Gör.

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