Kerim Kürşat ÇEVİK, Mustafa BOĞA, Hasan Erdinç KOÇER, Aykut BURGUT

Bilgisayar Destekli Otomatik Yumurta Döllülük Kontrolü

Çalışmada kuluçka makinesinde yumurtaların 0-5 gün aralığında döllülük kontrolünün kolay elde edilebilen ve az maliyetli araçlar kullanılarak görüntü işleme teknikleri ile tespit edilmesi amaçlanmıştır. Denemede, ev tipi standart kuluçka makinesi içine farklı zamanlarda görüntüleri alınan 15 yumurtadan oluşan üç farklı veri seti hazırlanmıştır. Yumurta görüntülerinin işlenmesinde çeşitli filtreleme ve morfoloji yöntemleri, gri seviye dönüşümü ve dinamik eşikleme yöntemi kullanılmıştır. Ayrıca probleme dayalı özgün görüntü işleme kodları yazılmıştır. Elde edilen binary görüntülerin beyaz/siyah oranları döllülük kontrolünü belirlemede kullanılmıştır. Deneysel sonuçlara göre ilk veri setinde 3. gün %73.34, 4. gün %100, ikinci veri setinde 3. gün %93.34, 4. gün %93.34 ve üçüncü veri setinde 3. gün %93.34, 4. gün %100 doğrulukla döllülük durumları tespit edilmiştir. Elde edilen sonuçlar değerlendirildiğinde, yumurta döllülük kontrolünün az maliyetli ve edinilebilir araçlar ile başarılı bir şekilde otomatikleştirilebileceği görülmüştür.

Computer-Assisted Automatic Egg Fertility Control

This research aimed to determine the fertilization control of the eggs in an incubator between 0th and 5th days by image processing techniques via low-priced tools. Three different datasets that were composed of eggs whose images taken at different times in the incubator were prepared. Several filtering and morphology methods, gray level conversion and dynamic thresholding were utilized to process the 15 egg images. Moreover, the original processing codes based on the problem were given. White and Black percentages of binary images were utilized to determine the egg control. According to the test results, for the first dataset; 73.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy was achieved on the fourth day, for the second dataset; 93.34% of fertility accuracy was achieved on the third day; 93.34% of fertility accuracy was achieved again on the fourth day; for the third dataset, 93.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy again was achieved on the fourth day. When the results were evaluated, it was seen that egg fertility has been determined successfully automated with low cost tools.

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