Comparative Analysis of Artificial Intelligence and Nonlinear Models for Broiler Growth Curve

Abstract

Numerous mathematical expressions for growth models have been developed, but each has its own characteristics and limitations. Therefore, this study has investigated whether artificial intelligence (AI) methods can be an alternative to these models. To this aim, four nonlinear (NL) models (logistic, Richards, Gompertz-Laird, and von Bertalanffy) and three AI techniques — artificial neural networks (ANN), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC) — were used to analyze growth. Some statistical methods, including the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the model performance. As a result of the study, it was determined that the ANFIS-SC model yielded a better fit with the broiler data due to its low MAE, RMSE, and MAPE values (7.68 g, 11.93 g, and 1.06%, respectively). The overall recommendation of this study is that the AI models could be used as an alternative to determine a broiler growth curve.

Keywords

• Growth curve
• broiler
• artificial intelligence
• regression model

Etlik Piliç Büyüme Eğrisinin Tahmininde Yapay Zeka ve Doğrusal Olmayan Modellerin Karşılaştırmalı Analizi

Öz

Büyüme modelleri için çok sayıda matematiksel ifade geliştirilmiştir, ancak her birinin kendine has özellikleri ve sınırlamaları bulunmaktadır. Dolayısıyla bu çalışmada yapay zeka (YZ) yöntemlerinin bu modellere alternatif olup olamayacağı araştırılmıştır. Bu amaçla büyümeyi analiz etmek için dört farklı doğrusal olmayan model (NL) (lojistik, Richards, Gompertz-Laird ve von Bertalanffy) ve üç farklı YZ tekniği - yapay sinir ağları (YSA) ve uyarlamalı sinirsel bulanık çıkarım sisteminin farklı yöntemleri ( ızgara bölümleme (ANFIS-GP) ve eksiltici kümeleme (ANFIS-SC)) kullanılmıştır. Modellerin performansını değerlendirmek için ortalama mutlak hata (MAE), ortalama karekök hata (RMSE) ve ortalama mutlak yüzde hata (MAPE) gibi bazı istatistiksel yöntemler ele alınmıştır. Çalışma sonucunda ANFIS-SC modelinin en düşük MAE, RMSE ve MAPE değerleri (sırasıyla 7.68 g, 11.93 g ve %1.06) ile gerçek ağırlık verileriyle daha iyi uyum sağladığı tespit edilmiştir. Sonuç olarak YZ modellerinin etlik piliç büyüme eğrisini belirlemek için alternatif olarak kullanılabileceği belirlenmiştir.

Anahtar Kelimeler

• Büyüme eğrisi
• etlik piliç
• yapay zeka
• regresyon modeli

Kaynakça

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