Automatic Classification of Healthy and Sick Broilers in Terms of Avian Influenza by Using Neural Networks

Yıl 2022, Cilt: 4 Sayı: 2, 212 - 226, 26.10.2022

Yalçın Işık Ahmet Kayabaşı

https://doi.org/10.46387/bjesr.1157160

Öz

Poultry meat containing low fat and high protein is an important and economical protein source in providing the animal protein requirement for human nutrition. The frequent emergence of poultry diseases such as avian influenza is the feature of fast-spread in farms seriously threatens both the economy and human health. In this study, neural network (NNs) models are proposed for the classification of broiler chickens as healthy and sick for earlier detection of poultry diseases. The NNs used in the classification are artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). In the literature, the data set which includes seven visual features were acquired through the IPTs and was used for training, testing, and validating the process of NN models. The results point out that, the computer vision-based application using NNs is successfully classified the broilers in terms of their health conditions.

Anahtar Kelimeler

Avian Influenza, Broiler chicken, Classification, Neural networks

Sağlıklı ve Hasta Etlik Piliçlerin Kuş Gribi Açısından Yapay Sinir Ağları Kullanarak Otomatik Sınıflandırılması

Öz

Düşük yağ ve yüksek protein içeren kanatlı eti, insan beslenmesi için hayvansal protein ihtiyacının sağlanmasında önemli ve ekonomik bir protein kaynağıdır. Çiftliklerde hızlı yayılma özelliği olan kuş gribi gibi kanatlı hastalıklarının sıklıkla ortaya çıkması hem ekonomiyi hem de insan sağlığını ciddi şekilde tehdit etmektedir. Bu çalışmada, kanatlı hastalıklarının erken tespiti için etlik piliçlerin sağlıklı ve hasta olarak sınıflandırılması için sinir ağı (NN'ler) modelleri önerilmiştir. Sınıflandırmada kullanılan NN'ler yapay sinir ağı (YSA), uyarlanabilir nöro-bulanık çıkarım sistemi (ANFIS) ve destek vektör makinesidir (SVM). Literatürde, IPT'ler aracılığıyla yedi görsel özellik içeren veri seti elde edilmiş ve NN modellerinin eğitimi, test edilmesi ve doğrulanması için kullanılmıştır. Sonuçlar, NN'leri kullanan bilgisayarlı görü tabanlı uygulamanın, piliçleri sağlık koşulları açısından başarıyla sınıflandırdığını göstermektedir.

Anahtar Kelimeler

Kuş Gribi, Etlik Piliç, Sınıflandırma, Yapay Sinir Ağları

Kaynakça

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