Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase

Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour.

[1]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[2]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Brittney N. Keel,et al.  Genome-wide association of changes in swine feeding behaviour due to heat stress , 2018, Genetics Selection Evolution.

[4]  I. Kyriazakis,et al.  To split behaviour into bouts, log-transform the intervals , 1999, Animal Behaviour.

[5]  J. A. Nienaber,et al.  GROUP FEEDING BEHAVIOR OF SWINE , 1991 .

[6]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[7]  Ilias Kyriazakis,et al.  Analysis of the feeding behavior of pigs using different models , 2000, Physiology & Behavior.

[8]  J. van Milgen,et al.  Modelling heat production and energy balance in group-housed growing pigs exposed to low or high ambient temperatures , 2001, British Journal of Nutrition.

[9]  Robert A. Schowengerdt,et al.  A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  Ilias Kyriazakis,et al.  Early detection of health and welfare compromises through automated detection of behavioural changes in pigs , 2016, Veterinary journal.

[12]  M. Berdoy.  Defining bouts of behaviour: a three-process model , 1993, Animal Behaviour.

[13]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[14]  Roger A. Eigenberg,et al.  Analysis of feeding behavior of group housed growing-finishing pigs , 2013 .

[15]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[16]  Anders Ringgaard Kristensen,et al.  A model for monitoring the condition of young pigs by their drinking behaviour , 2005 .

[17]  Allan Pinkus,et al.  Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.

[18]  T. M. Brown-Brandl,et al.  Development of a Livestock Feeding Behavior Monitoring System , 2011 .

[19]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[20]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[21]  J. Noblet,et al.  Voluntary feed intake and feeding behaviour of group-housed growing pigs are affected by ambient temperature and body weight. , 2000 .

[22]  Ajith Abraham,et al.  Artificial Neural Networks , 2005 .

[23]  A. Comrie.  Comparing Neural Networks and Regression Models for Ozone Forecasting , 1997 .

[24]  Y. R. Chen,et al.  EATING DYNAMICS OF GROWING-FINISHING SWINE , 1990 .

[25]  L. Keeling,et al.  Using data from electronic feeders on visit frequency and feed consumption to indicate tail biting outbreaks in commercial pig production. , 2013, Journal of animal science.

[26]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[27]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).