Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Abstract

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms

“…They reported classification accuracies of 85 to 86%, with sensitivity and specificity of 85%, for a multilayer perceptron with 2 hidden layers. Among the machine learning methods used in the animal sciences, artificial neural networks are the most frequently used, with applications such as predicting milk yield in dairy cows (Lacroix et al, 1995;Grzesiak et al, 2006;Gianola et al, 2011), classifying mastitis cases (Yang et al, 1999), classifying lameness in horses (Suchorski-Tremblay et al, 2001), predicting the slaughter weight of bull calves (Adamczyk et al, 2005), identifying SNP associated with chicken mortality (Long et al, 2009), and real-time prediction of breeding values in dairy cattle (Shahinfar et al, 2012).…”

Comparison of artificial neural network and K-means for clustering dairy cattle

“…In animal science, ANNs have been successfully applied to various areas, such as diagnosis of diseases, such as mastitis and lameness (Yang et al, 1999;Cavero et al, 2008;Sun, 2008;Hassan et al, 2009;Roush et al, 2001); the prediction of forward-looking traits (Grzesiak et al, 2003;Salehi et al, 1998;Sanzogni and Kerr, 2001;Kominakis et al, 2002;Hosseinia et al, 2007;Görgülü, 2012); animal breeding studies (Shahinfar et al, 2012;Salehi et al, 1997;Grzesiak et al, 2010); the prediction of the nutrient content in manure (Chen et al, 2008(Chen et al, , 2009; and oestrus detection (Krieter et al, 2006).…”

Seed yield prediction of sesame using artificial neural network

“…In contrast to the traditional methods such MLR and PA models, the application of artificial intelligence (AI) models such as artificial neural networks (ANN), genetic expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) were recently attracted the attention of researchers in agriculture science (Azamathulla and Ghani, 2011;Shahinfar et al, 2012;Emamgholizadeh et al, 2013a,b;Samadianfard et al, 2014;Silva et al, 2014;Iquebal et al, 2014). Alvarez (2007) used the ANN approach to predict average regional yield and production of wheat in the Argentine Pampas.…”