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Benchmarking analysis of CNN models for bread wheat varieties

European Food Research and Technology · Mart 2023

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YÖKSİS Kayıtları
Benchmarking analysis of CNN models for bread wheat varieties
EUROPEAN FOOD RESEARCH AND TECHNOLOGY · 2023 SCI-Expanded
Doç. Dr. ALİ YAŞAR →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
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Antifungal properties of some herb decoctions
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The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded 14 atıf
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2005 ISSN: 1438-2377 SSCI 15 atıf
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2020 ISSN: 1438-2377 SCI
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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
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Volatile profile evolution and sensory evaluation of traditional skinbag Tulum cheeses manufactured in Karaman mountainous region of Turkey during ripening
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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
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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
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A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
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Detection of fish freshness using artificial intelligence methods
2023 ISSN: 1438-2377 SCI-Expanded Q2
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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
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Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
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Makale Bilgileri

Dergi European Food Research and Technology
ISSN14382377
Yayın TarihiMart 2023
Cilt / Sayfa249 · 749-758
DOI10.1007/s00217-022-04172-y
Scopus ID2-s2.0-85142087289
Özet Most of the wheat produced and consumed worldwide is generally bread wheat and is used for bread making. Bread wheat varieties can affect the quality of bread. When comparing bread wheat to other varieties, there may be differences in taste, cost, and impact on human health. This study aims to classify bread wheat varieties using deep learning methods. Wheat cultivars used in this research (‘Ayten Abla’, ‘Bayraktar 2000’, ‘Hamitbey’, ‘Şanlı’, and ‘Tosunbey’) were obtained from the Central Field Crop Research Institute, Ministry of Agriculture and Forestry, Republic of Türkiye. First, a dataset of 8354 images of these wheat varieties was created. Then, the images in this dataset were trained with tree different Convolutional Neural Networks (CNNs) using the transfer learning method. The CNN models used are Inception-V3, Mobilenet-V2, and Resnet18, and the classification accuracies obtained are 97.37%, 97.07%, and 97.67%, respectively. Finally, the images not used for training and validation of the CNN models were segmented using image processing techniques. The segmented images were classified as bread wheat and unidentified seeds in the Resnet18 CNN model.

Yazarlar (1)

1
Ali Yasar

Anahtar Kelimeler

Bread wheat Classification CNN Inception-V3 Mobilenet-V2 Resnet18

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)
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Metrikler

31
Atıf
1
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

AY
ALİ YAŞAR
Doç. Dr.

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