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Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models

European Food Research and Technology · Ocak 2024

YÖKSİS DOI Eşleşmesi Bulundu

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

YÖKSİS Kayıtları
Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models
European Food Research and Technology · 2024 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models
European Food Research and Technology · 2024 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models
European Food Research and Technology · 2024 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models
European Food Research and Technology · 2024 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
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 TarihiOcak 2024
Cilt / Sayfa250 · 97-110
DOI10.1007/s00217-023-04369-9
Scopus ID2-s2.0-85172709489
Özet In many crops worldwide, including hazelnuts, the majority of stages in production and delivery to end-users are conducted either manually or with machine equipment lacking the advancements brought by technology. Non-destructive, fast, and reliable methods, particularly deep learning algorithms, have emerged as prominent techniques for determining product quality and classification in fruits, vegetables, and cereal products in recent years. This study aims to classify hazelnuts using deep learning algorithms, thereby minimizing the labor, time, and cost expended during the sorting process. Hazelnut images were obtained from Giresun, Ordu, and Van hazelnut varieties. The dataset consists of 1165 images of Giresun, 1324 images of Ordu, and 1138 images of Van hazelnut varieties. The classification was performed using deep learning models such as InceptionV3 and ResNet50. To combine the classification capabilities of the models, an InceptionV3 + ResNet50 data fusion model was created using the data fusion method. In addition, feature reduction processes were conducted by adding a convolutional layer to the data fusion model to decrease the number of features. The classification was conducted using a total of 3627 images, resulting in a 100% classification accuracy. Furthermore, the classification times of all models were analyzed. Based on these analyses, the 1024 reduced features data fusion model with 100% classification accuracy exhibited the shortest classification time. This model was selected, and a mobile application was developed for easy on-field hazelnut classification. The hazelnut classification performed using deep learning algorithms in the application will facilitate the work of both non-experts and professionals in industrial and personal domains. Through these methods, patents for products and devices developed for use in different industries can be obtained, thereby increasing the economic value added of our country.

Yazarlar (7)

1
Bunyamin Gencturk
ORCID: 0009-0001-0944-2898
2
Sadiye Arsoy
ORCID: 0000-0002-1499-868X
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
4
Ilkay Cinar
ORCID: 0000-0003-0611-3316
5
Ramazan Kursun
ORCID: 0000-0002-6729-1055
6
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
7
Murat Koklu
ORCID: 0000-0002-2737-2360

Anahtar Kelimeler

Artificial intelligence Computer vision Convolutional neural networks Deep learning Hazelnut classification

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)
Dergi sayfasına git

Metrikler

26
Atıf
7
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

RK
RAMAZAN KURŞUN
Öğr. Gör.

Hızlı Erişim

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