Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
European Food Research and Technology · Kasım 2022
YÖKSİS Kayıtları
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
European Food Research and Technology · 2022 SCI-Expanded
Arş. Gör. MUSA DOĞAN →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
European Food Research and Technology · 2022 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
European Food Research and Technology · 2022 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
European Food Research and Technology · 2022 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
EUROPEAN FOOD RESEARCH AND TECHNOLOGY · 2022 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
European Food Research and Technology · 2022 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded
Dr. Öğr. Üyesi İRFAN ÖZER →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded 14 atıf
Dr. Öğr. Üyesi İRFAN ÖZER →
The effect of various types of poultry pre and post rigor meats on emulsification capacity water holding capacity and cooking loss
2005 ISSN: 1438-2377 SSCI 15 atıf
Prof. Dr. CEMALETTİN SARIÇOBAN →
The effect of irrigation and harvest time on bioactive properties of olive fruits issued from some olive varieties grown in Mediterranean region
2020 ISSN: 1438-2377 SCI
Doç. Dr. NURHAN USLU →
The effect of irrigation and harvest time on bioactive properties
of olive fruits issued from some olive varieties grown in Mediterranean
region
2020 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Volatile profile evolution and sensory evaluation of traditional skinbag Tulum cheeses manufactured in Karaman mountainous region of Turkey during ripening
2021 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. TALHA DEMİRCİ →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Arş. Gör. MUSA DOĞAN →
The influence of decoction and infusion methods and times on antioxidant activity, caffeine content and phenolic compounds of coffee brews
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. NURHAN USLU →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded Q2
Dr. Öğr. Üyesi İRFAN ÖZER →
A review: benefit and bioactive properties of olive (Olea europaea L.) leaves
2016 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. İLKER ALİ ÖZKAN →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Benchmarking analysis of CNN models for bread wheat varieties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →
Classification of Deep Image Features of Lentil Varieties with Machine Learning Techniques
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Detection of fish freshness using artificial intelligence methods
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →
Makale Bilgileri
ISSN14382377
Yayın TarihiKasım 2022
Cilt / Sayfa248 · 2707-2725
Scopus ID2-s2.0-85135560391
Özet
There are many types of haricot beans, the nutrient consumed all over the world. Each type differs in terms of features such as taste, size, economic value, etc. But even if they are different types, bean grains are frequently confused with each other. For these reasons, it is important to separate the bean grains of different species. For this purpose, a haricot bean dataset consisting of 33,064 images of 14 different bean types was created. By using these images, 3 different pre-trained Convolutional Neural Networks (CNN) were trained via the transfer learning method. Within the scope of the study, InceptionV3, VGG16, and VGG19 CNN models were used. These models were utilized for both end-to-end classification and extraction of image features. Firstly, the images were classified via Inception V3, VGG16, and VGG19 models. As a result of this classification, 84.48%, 80.63%, and 81.03% classification success were obtained from InceptionV3, VGG16, and VGG19 models, respectively. Secondly, the image features of these 3 models were taken from the layer just before the classification layer. Then, these features were given as input to the Support Vector Machine (SVM) and Logistic Regression (LR) models. Images were classified using six different models, InceptionV3 + SVM, VGG16 + SVM, VGG1 + SVM and InceptionV3 + LR, VGG16 + LR, VGG1 + LR. Classification successes obtained from InceptionV3 + SVM, VGG16 + SVM, and VGG19 + SVM were 79.60%, 81.97%, 80.64%, respectively. And, the classification successes obtained from InceptionV3 + LR, VGG16 + LR, and VGG19 + LR were 82.35%, 83.71%, and 83.54%, respectively. The InceptionV3, among all models, was determined to be the best classification model with a classification success of 84.48%. On the other hand, the model with the lowest classification success was determined to be the InceptionV3 + SVM. Detailed analysis of the created models was also carried out with precision, recall, and F-1 score metrics. It is thought that the proposed models can be used to distinguish haricot bean types in a quick and accurate way. Furthermore, the proposed computer vision methods can be combined with robotic systems and used to the distinction of bean types. By means of image processing, varieties can be determined on conveyor belts, and dry bean varieties can be purified with delta robots.
Yazarlar (6)
1
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
2
Musa Dogan
3
Ilkay Cinar
ORCID: 0000-0003-0611-3316
4
Ramazan Kursun
ORCID: 0000-0002-6729-1055
5
Ilker Ali Ozkan
6
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Classification
Deep features
Dry bean
Hybrid methods
Machine learning
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
European Food Research and Technology
Q1
SJR Skoru0,744
H-Index131
YayıncıSpringer Science and Business Media Deutschland GmbH
ÜlkeGermany
Food Science (Q1)
Industrial and Manufacturing Engineering (Q1)
Biochemistry (Q2)
Biotechnology (Q2)
Chemistry (miscellaneous) (Q2)
Metrikler
42
Atıf
6
Yazar
5
Anahtar Kelime