Sajad SABZİ, Yousef ABBASPOUR-GİLANDEH, Yousef ABBASPOUR-GILANDEH, Hossein JAVADIKIA, Hossein JAVADİKİA, Hadis HAVASKHAN, Hadis HAVASKHAN

Görüntü İşleme ve ANFIS ile Emperor Elmasının Otomatik Sınıflandırılması

Ağırlık tabanlı meyve sınıflandırma, paketleme ve pazarlanmanın iyileştirilmesi açısından önemli bir terimdir. Ağırlıklarına göre sınıflandırma doğrudan veya dolaylı yöntemlerle gerçekleştirilebilir. Bu çalışma için, Kirmanşah, İran’da (Boylam: 7.03 °E; Enlem: 4.22 °N). bir meyve bahçesinden rastgele 100 Emperor Elma örneği seçilmiştir. Tüm testler Ziraat Mühendisliği Fakültesi, Razi Üniversitesi, Kirmanşah, İran Fizik Laboratuarında yapılmıştır. Her elma için görüntü işleme ile ondört parametre elde edilmiştir. ANFIS ve doğrusal regresyon yöntemleri kullanılarak çeşitli ağırlık modelleri geliştirilmiştir. En iyi model sırasıyla ANFIS, doğrusal ve doğrusal olmayan regresyon için, R2, SSE, ve MSE için 0.990, 276.58, 13.17, 0.856, 15980.96, 166.47 ve 0.791, 24512.16, 255.35 şeklindedir. Yani, makine görme sistemi ile meyve ile temas etmeden ağırlık tabanlı elma sınıflandırması sağlanabilir. Mekanik ve elektrik sistemleri üzerinden bu sistemin faydaları şunlardır: 1- Farklı boyutlarda gruplar için makinenin tekrar kalibrasyon kolaylığı, 2- Dolaylı sınıflandırma kullanılarak daha doğru ağırlık ölçümü ve yüksek çalışma hızına ulaşma.

Anahtar Kelimeler:

SPSS, Paketleme, Pazarlama, Yapay görme, Bulanık çıkarım sistemi, Ayırma

Automatic Grading of Emperor Apples Based on Image Processing and

Mass-based fruit classification is important in terms of improving packaging and marketing. Mass sizing can beaccomplished by direct or indirect methods. In this study, 100 samples of Emperor Apples were randomly selected froman orchard in Kermanshah, Iran (longitude: 7.03 °E; latitude: 4.22 °N). All tests were carried out in Physical Laboratory,Faculty of Agriculture Engineering, Razi University, and Kermanshah, Iran. Fourteen parameters were obtained byimage processing for each apple. Several mass modeling were made using ANFIS and linear regression methods. In thebest model for ANFIS, linear and nonlinear regression, R2, SSE, and MSE were 0.990, 276.58, 13.17, 0.856, 15980.96,166.47 and 0.791, 24512.16, 255.35, respectively. So, a mass-based sorting system was proposed with machine visionsystem and using ANFIS method that could obtain apple mass without contact with the fruit. Benefits of this system overmechanical and electrical systems were: 1- Easier recalibration of the machine to the groups with different sizes, and2- Reaching more accurate mass measurement and higher operating speed using indirect grading.

Keywords:

SPSS; Packaging; Marketing; Machine vision; Fuzzy inference system; Sorting, SPSS, fUZZY,

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