Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Mathematical Problems in Engineering · Ocak 2021
YÖKSİS Kayıtları
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Mathematical Problems in Engineering · 2021 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Mathematical Problems in Engineering · 2021 SCI-Expanded
ÖĞRETİM GÖREVLİSİ RAMAZAN KURŞUN →
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Mathematical Problems in Engineering · 2021 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Mathematical Problems in Engineering · 2021 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiMathematical Problems in Engineering
Yayın TarihiOcak 2021
Cilt / Sayfa2021
Scopus ID2-s2.0-85119920509
Erişim🔓 Açık Erişim
Özet
A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.
Yazarlar (4)
1
Murat Koklu
ORCID: 0000-0002-2737-2360
2
Ramazan Kursun
ORCID: 0000-0002-6729-1055
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
4
Ilkay Cinar
ORCID: 0000-0003-0611-3316
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
52
Atıf
4
Yazar