Görüntü İşleme ve ANFIS ile Emperor Elmasının Otomatik Sınıflandırılması

Year 2015, Volume: 21 Issue: 3, 326 - 336, 12.08.2015

Sajad Sabzi Yousef Abbaspour-gilandeh Yousef Abbaspour-gılandeh Hossein Javadıkıa Hossein Javadikia Hadis Havaskhan Hadis Havaskhan

https://doi.org/10.1501/Tarimbil_0000001335

Cited By: 5

Abstract

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.

Keywords

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

Automatic Grading of Emperor Apples Based on Image Processing and

Abstract

Mass-based fruit classification is important in terms of improving packaging and marketing. Mass sizing can be

accomplished by direct or indirect methods. In this study, 100 samples of Emperor Apples were randomly selected from

an 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 by

image processing for each apple. Several mass modeling were made using ANFIS and linear regression methods. In the

best 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 vision

system and using ANFIS method that could obtain apple mass without contact with the fruit. Benefits of this system over

mechanical and electrical systems were: 1- Easier recalibration of the machine to the groups with different sizes, and

2- 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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