Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q2
Automatic classification of walnut (Juglans Regia L.) species using deep learning methods
Journal of Food Measurement and Characterization · Ağustos 2025
YÖKSİS Kayıtları
Automatic classification of walnut (Juglans Regia L.) species using deep learning methods
Journal of Food Measurement and Characterization · 2025 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Automatic classification of walnut (Juglans Regia L.) species using deep learning methods
Journal of Food Measurement and Characterization · 2025 SCI-Expanded
Prof. Dr. NURETTİN DOĞAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
The effect of microwave and conventional drying on antioxidant activity phenolic compounds and mineral profile of date fruit (Phoenix dactylifera L.) flesh
2017 ISSN: 2193-4126 SCI-Expanded
Doç. Dr. NURHAN USLU →
Mathematical modeling of thin layer drying of carrot slices by forced convection
2017 ISSN: 2193-4126 SCI
Prof. Dr. MEHMET MUSA ÖZCAN →
Effect of species on total phenol, antioxidant activity and phenolic compounds of different wild onion bulbs
2018 ISSN: 2193-4126 SCI-Expanded
Doç. Dr. NURHAN USLU →
Effect of species on total phenol, antioxidant activity and phenolic compounds of different wild onion bulbs
2018 ISSN: 2193-4126 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Bioactive compounds, antioxidant activity and some physicochemical properties of theseed and seed-oil of Mahonia aquifolium berries
2019 ISSN: 2193-4126 SCI-Expanded
Prof. Dr. HACER ÇOKLAR →
Central composite design and response surface methodologyfor the optimization of Ag-HPLC/ELSD method for triglycerideprofiling
2017 ISSN: 2193-4126 SCI-Expanded Q3
Prof. Dr. HÜSEYİN KARA →
Effect of soxhlet and cold press extractions on the physico-chemicalcharacteristics of roasted and non-roasted chia seed oils
2019 ISSN: 2193-4126 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Central composite design and response surface methodology for the optimization of Ag-HPLC/ELSD method for triglyceride profiling
2017 ISSN: 2193-4126 SCI-Expanded
Prof. Dr. HÜSEYİN KARA →
Effect of sonication times and almond varieties on bioactive properties, fatty acid and phenolic compounds of almond kernel extracted by ultrasound-assisted extraction system
2021 ISSN: 2193-4126 SCI-Expanded Q3
Doç. Dr. NURHAN USLU →
Influence of germination on bioactive properties, phytochemicals and mineral contents of Tigernut (Cyperus esculentus L.) tuber and oils
2021 ISSN: 2193-4126 SCI-Expanded Q3
Doç. Dr. NURHAN USLU →
Use of herbal essential oil and extracts as antioxidant sources in quality stabilization of extra virgin olive oil stored in different time and packages
2022 ISSN: 2193-4126 SCI-Expanded Q3
Doç. Dr. NURHAN USLU →
Some physicochemical and phytochemical properties of Syringa vulgaris L. flower tea: influence of flower drying technique, brewing method and brewing time
2022 ISSN: 2193-4126 SCI-Expanded Q3
Prof. Dr. MEHMET AKBULUT →
Prediction of moisture content of wet and dried nixtamal after alkaline cooking process by using artificial neural network
2022 ISSN: 2193-4126 SCI-Expanded Q2
Doç. Dr. MUSTAFA ŞAMİL ARGUN →
Use of herbal essential oil and extracts as antioxidant sources
in quality stabilization of extra virgin olive oil stored in different time
and packages
2022 ISSN: 2193-4126 SCI-Expanded Q3
Prof. Dr. MEHMET MUSA ÖZCAN →
Effect of ultrasound and microwave pretreatments on some bioactive properties of beef protein hydrolysates
2023 ISSN: 2193-4126 SCI-Expanded Q2
Prof. Dr. CEMALETTİN SARIÇOBAN →
Mathematical modeling of thin layer drying of carrot slices by forced convection
2016 ISSN: 2193-4126 SCI
Prof. Dr. HAKAN OKYAY MENGEŞ →
Quality characteristics of functional snack foods prepared from hazelnut shell and teff flour
2023 ISSN: 2193-4126 SCI-Expanded Q2
Prof. Dr. SULTAN ARSLAN TONTUL →
The effect of fermentation with different additives on bioactive compounds, antioxidant activity, phenolic component, fatty acid composition and mineral substance contents of capers fruits
2023 ISSN: 2193-4126 SCI-Expanded Q2
Doç. Dr. NURHAN USLU →
Physico‑chemical properties, tocopherol contents, fatty acid
composition and phenolic compounds of olive oil as affected
by papain and cellulase application
2023 ISSN: 2193-4126 SCI-Expanded Q2
Prof. Dr. MEHMET MUSA ÖZCAN →
The effect of fermentation with different additives on bioactive
compounds, antioxidant activity, phenolic component, fatty acid
composition and mineral substance contents of capers fruits
2023 ISSN: 2193-4126 SCI-Expanded Q2
Prof. Dr. MEHMET MUSA ÖZCAN →
Makale Bilgileri
ISSN21934126
Yayın TarihiAğustos 2025
Cilt / Sayfa19 · 6119-6140
Scopus ID2-s2.0-105007072269
Erişim🔓 Açık Erişim
Özet
Walnut is an agricultural product with high economic value on a global scale. Walnut species classification is essential for research, conservation, and quality control, yet traditional methods rely heavily on manual identification, which is a time-consuming procedure and subject to human mistakes. With technological developments in the agricultural sector, making use of deep learning algorithms in the classification of products such as fruits, vegetables and grains were becoming increasingly widespread. In this study, it is aimed to automatically classify walnut species using pre-trained deep learning models. Thus, it is aimed at reducing the loss of time, workload and error rates in sorting processes. For this purpose, a dataset consisting of images belonging to Chandler, Kaman1, Fernor, Yalova3 and Maras18 walnut species was created. The dataset consists of 2540 images in total. The images were trained using VGG16, VGG19, ResNet-50, DenseNet-121, and Xception models. The classification was performed using pre-trained deep learning architectures, including VGG16, VGG19, ResNet-50, DenseNet-121, and Xception. Among these models, ResNet-50 delivered the best performance with an accuracy of 97.95% on the original dataset, while the Xception model excelled with 98.54% accuracy when trained with a weighted loss function and 98.27% accuracy with data augmentation. These findings highlight the effectiveness and reliability of ResNet-50 and Xception models for automated walnut species classification. The results underscore the potential of deep learning technologies in improving agricultural practices by offering faster, more accurate, and less labor-intensive alternatives to traditional methods. In comparison, machine learning algorithms such as SVM, RF, and k-NN achieved lower accuracies, with SVM performing best among them at 90.10%. The study provides an important contribution to the use of deep learning technologies in agricultural production processes and suggests solutions that can increase the efficiency of traditional manual methods.
Yazarlar (3)
1
Halil Kilif
ORCID: 0000-0001-6261-6992
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Nurettin Doğan
ORCID: 0000-0002-8267-8469
Anahtar Kelimeler
Agriculture
Deep Learning
Transfer Learning
Walnut Classification
Walnut Species
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Food Measurement and Characterization
Q2
SJR Skoru0,620
H-Index66
YayıncıSpringer Science + Business Media
ÜlkeUnited States
Chemical Engineering (miscellaneous) (Q2)
Food Science (Q2)
Industrial and Manufacturing Engineering (Q2)
Safety, Risk, Reliability and Quality (Q2)
Metrikler
4
Atıf
3
Yazar
5
Anahtar Kelime