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Scopus YÖKSİS DOI Eşleşti SJR Q1

Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features

Computers and Electronics in Agriculture · Kasım 2019

YÖKSİS DOI Eşleşmesi Bulundu

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YÖKSİS Kayıtları
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2019 SCI-Expanded
Dr. Öğr. Üyesi ESRA KAYA ERDOĞAN →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
Computers and Electronics in Agriculture · 2019 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2019 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 10 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis
2011 ISSN: 01681699 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. MERT DEDEOĞLU →
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
2020 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry Bean Cultivars Classification Using Deep CNN Features and Salp Swarm Algorithm Based Extreme Learning Machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi ESRA KAYA ERDOĞAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Dergi Computers and Electronics in Agriculture
ISSN01681699
Yayın TarihiKasım 2019
Cilt / Sayfa166
DOI10.1016/j.compag.2019.105016
Scopus ID2-s2.0-85072606980
Özet Wheat is the main ingredient of most common food products in our daily lives and obtaining good quality wheat kernels is an important matter for the production of food supplies. In this study, type-1252 durum wheat kernels which have vast harvest areas in Turkey and is the principal ingredient of pasta and semolina products were examined and classified to obtain top quality wheat kernels based on their vitreousness. Also, top quality provision of food supplies means that the products must be refined from all foreign materials so a classification process has been applied to extract foreign materials from wheat kernels. In this study, we have used a total of 236 morphological, colour, wavelet and gaborlet features to classify vitreous, starchy durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amount of features based on the feature rank list obtained with ANOVA test. The data we have used in this study was video images of wheat kernels and foreign objects present on a conveyor belt camera system with illumination provided by daylight colour powerleds. The maximum classification accuracy was 93.46% obtained with 210 feature neural network function which was generated and applied on the video containing a mixture of wheat kernels and foreign objects.

Yazarlar (2)

1
Esra Kaya
ORCID: 0000-0003-1401-9071
2
Ismail Saritas

Anahtar Kelimeler

ANN Durum wheat Gaborlet Vitreousness Wavelet

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Computers and Electronics in Agriculture
Q1
SJR Skoru1,834
H-Index188
YayıncıElsevier B.V.
ÜlkeNetherlands
Agronomy and Crop Science (Q1)
Animal Science and Zoology (Q1)
Computer Science Applications (Q1)
Forestry (Q1)
Horticulture (Q1)
Dergi sayfasına git

Metrikler

56
Atıf
2
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

İS
İSMAİL SARITAŞ
Prof. Dr.

Hızlı Erişim

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