Model of Combined IPT and NNLVQ for Classification of Healthy and Sick Broilers In Terms of Avian Influenza

Öz

The poultry meat is an important and economical protein source in providing the animal protein requirement for human nutrition. The poultry diseases such as avian influenza that is feature of fast-spread in farms seriously threatens both the economy and human health. The avian influenza must be detected early because it spreads rapidly. Earlier detection of poultry diseases has become more possible with the development of systems combining image processing techniques (IPTs) and artificial intelligence techniques (AITs). In this study, the neural network (NN) based model using learning vector quantization (LVQ) structure are proposed for classification of broiler chickens as healthy and sick. In the literature, seven main visual feature parameters that indicate the health status of broilers were acquired through the IPTs. The 300 data set includes seven visual features is used for training (#260), testing (#20) and validating (#20) process of NNLVQ model. The classification performance of neural network (NN) using learning vector quantization (NNLVQ) is compared with IPT regard to its efficiency and accuracy. In the training process, the NNLVQ model classifies the broilers in terms of avian influenza with accuracy error (AE) of 0.384%. The results point out that, the IPT based application using NNLVQ is successfully classified the broilers in terms of their health conditions.

Anahtar Kelimeler

• broiler chicken
• classification
• avian influenza
• neural network
• learning vector quantization

Kaynakça

1. Fanatico, A. C., Pillai, P. B., Emmert, J. L., & Owens, C. M. (2007). Meat quality of slow-and fast-growing chicken genotypes fed low-nutrient or standard diets and raised indoors or with outdoor access. Poultry science, 86(10), 2245-2255.
2. Zhuang, X., Bi, M., Guo, J., Wu, S., & Zhang, T. (2018). Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture, 144, 102-113.
3. Mollah, M. B. R., Hasan, M. A., Salam, M. A., & Ali, M. A. (2010). Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture, 72(1), 48-52.
4. Matin, H. R. H., Saki, A. A., Varkeshi, M. B., & Abyaneh, H. Z. (2013). Comparison and validation of artificial intelligent techniques to estimate intestinal broiler microflora. Neural Computing and Applications, 23(1), 61-66.
5. Aydin, A. (2017). Development of an early detection system for lameness of broilers using computer vision. Computers and Electronics in Agriculture, 136, 140-146.
6. Pereira, D. F., Miyamoto, B. C., Maia, G. D., Sales, G. T., Magalhães, M. M., & Gates, R. S. (2013). Machine vision to identify broiler breeder behavior. Computers and electronics in agriculture, 99, 194-199.
7. Ferraz, P. F. P., Yanagi Junior, T., Hernández Julio, Y. F., Castro, J. D. O., Gates, R. S., Reis, G. M., & Campos, A. T. (2014). Predicting chick body mass by artificial intelligence-based models. Pesquisa Agropecuária Brasileira, 49(7), 559-568.
8. Mortensen, A. K., Lisouski, P., & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319-326. Zhang, T., Bi, M., Guo, J. & Zhuang X. (2017). Data for: Broiler chickens posture feature extraction and disease early-warning algorithm, Mendeley Data, v1. http://dx.doi.org/10.17632/txjj8mwtz6.1.

9. Temel, T. (2017). A new classification algorithm: optimally generalized learning vector quantization (OGLVQ). Neural Network World, 27(6), 569-576.
10. Haykin, S. (1994). Neural networks: A comprehensive foundation. Macmillan College Publishing Company, New York, A.B.D..
11. Dursun, Ö. O., Toraman, S., & Türkoğlu, A. (2017). Comparison Of The Classification Performances Of Criminal Tendencies Of Schizophrenic Patients By Artificial Neural Networks And Support Vector Machine. European Journal of Technique, 7(2), 177-185.
12. Kilic, B. (2019). Impedance Image Reconstruction With Artificial Neural Network In Electrical Impedance Tomography. European Journal of Technique, 9(2), 137-144.