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Classification of deep image features of lentil varieties with machine learning techniques

European Food Research and Technology · Mayıs 2023

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YÖKSİS Kayıtları
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Classification of Deep Image Features of Lentil Varieties with Machine Learning Techniques
European Food Research and Technology · 2023 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

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 →
Antifungal properties of some herb decoctions
2000 ISSN: 1438-2377 SCI
Prof. Dr. NUH BOYRAZ →
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

Dergi European Food Research and Technology
ISSN14382377
Yayın TarihiMayıs 2023
Cilt / Sayfa249 · 1303-1316
DOI10.1007/s00217-023-04214-z
Scopus ID2-s2.0-85148070594
Özet Today, image classification methods are widely utilized on agricultural products or in agricultural applications. However, many of these methods based on traditional approaches remain unsatisfactory in terms of obtaining effective results. Within this context, this study aimed to classify lentil images by machine learning algorithms, a current and effective method. In line with this purpose, first of all, a camera system was prepared primarily and a dataset was created by recording lentil grains at 225 × 225 resolution via this system. The dataset contains a total of 33,938 data obtained from 3 lentil species as green, yellow, and red. SqueezeNet, InceptionV3, DeepLoc, and VGG16 architectures, among the CNN methods, were used in order to extract features from the recorded images. Lastly, Artificial Neural Network (ANN), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AB), and Decision Tree (DT) algorithms were utilized with the aim of creating models for lentil images’ classification. The classification success of the created machine learning models was calculated and the results were analyzed. The highest classification success with the deep features obtained from the SqueezeNet model, 99.80%, was achieved in the ANN algorithm. The results also revealed that grain size and shape features in image classification can yield much more detailed and precise data than can be obtained practically with manual quality assessment.

Yazarlar (6)

1
Resul Butuner
ORCID: 0000-0002-9778-2349
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
4
Ramazan Kursun
ORCID: 0000-0002-6729-1055
5
M. Hanefi Calp
ORCID: 0000-0001-7991-438X
6
Murat Koklu
ORCID: 0000-0002-2737-2360

Anahtar Kelimeler

Classification Deep learning Lentil Machine learning

Kurumlar

Ankara Beypazarı Fatih Vocational and Technical Anatolian High School
Ankara Turkey
Ankara Hacı Bayram Veli University
Ankara Turkey
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)
Dergi sayfasına git

Metrikler

42
Atıf
6
Yazar
4
Anahtar Kelime

Sistemimizdeki Yazarlar

RK
RAMAZAN KURŞUN
Öğr. Gör.

Hızlı Erişim

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