Scopus
YÖKSİS Eşleşti
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · Mayıs 2023
YÖKSİS Kayıtları
Classification of Deep Image Features of Lentil Varieties with Machine Learning Techniques
European Food Research and Technology · 2023 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
ÖĞRETİM GÖREVLİSİ RAMAZAN KURŞUN →
Classification of deep image features of lentil varieties with machine learning techniques
European Food Research and Technology · 2023 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Makale Bilgileri
DergiEuropean Food Research and Technology
Yayın TarihiMayıs 2023
Cilt / Sayfa249 · 1303-1316
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
Metrikler
23
Atıf
6
Yazar
4
Anahtar Kelime