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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

Görüntü İşleme Tekniklerine Dayalı Sinirsel-Bulanık Sistem Modeli Kullanarak Buğday Danelerinin Sınıflandırılması

How to cite: Kayabaşı A, Sabancı K, Toktaş A. Classification of wheat grains using image processing techniques based neuro-fuzzy system model. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(1): 57-61. DOI: 10.54856/jiswa.201805018

Full Text: PDF, in Turkish.

Total number of downloads: 512

Title: Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

Abstract: In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

Keywords: Classification; wheat grains; image processing technique; neuro-fuzzy system (NFS)


Başlık: Görüntü İşleme Tekniklerine Dayalı Sinirsel-Bulanık Sistem Modeli Kullanarak Buğday Danelerinin Sınıflandırılması

Özet: Bu çalışmada, görüntü işleme tekniklerine (GİT) dayalı Sugeno tip sinirsel-bulanık sistem (SBS) modeli kullanarak buğday danelerinin, ekmeklik ve makarnalık olarak sınıflandırılması işlemi sunuldu. SBS modelinin eğitim ve test sürecinde kullanılacak veri setini elde etmek için 200 buğday danesinin görüntüleri yüksek çözünürlüklü kamera ile alınmıştır. Uzunluk, genişlik, alan, çevre ve doluluk olmak üzere 5 adet boyut özelliği, GIT kullanılarak elde edilmiştir. Sonra, SBS modeli, 180 buğday danesinin boyut özelliği giriş olacak şekilde kullanılarak eğitilmiş ve modelin doğruluğu da geriye kalan 20 buğday danesi ile test edilmiştir. Önerilen SBS modeli, test sürecinde sayısal olarak 0.0312 ortalama mutlak hata (OMH) ve %100'lük bir doğruluk ile buğday danelerini ekmeklik ve makarnalık olarak sınıflandırmıştır. Bu sonuçlar GİT ve SBS modelinin buğday danelerinin ekmeklik ve makarnalık olarak sınıflandırılmasında başarılı bir şekilde kullanılabileceğini göstermektedir.

Anahtar kelimeler: Sınıflandırma; buğday daneleri; görüntü işleme teknikleri; sinirsel-bulanık sistem


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