OJAS  Vol.4 No.4 , July 2014
Using Artificial Neural Network to Predict Body Weights of Rabbits
Abstract: In this (modest) study, we developed artificial neural network (ANN) models for predicting body weight using various independent (input) variables in eight-week old New Zealand white purebred and crossbred rabbits. From the whole data sets of similar age groups, 75 percent were used to train the neural network model and 25 percent were used to test the effectiveness of the model. Five predictor variables were used viz, breed, sex, heart girth, body length and height at wither as input variables and body weight was considered as dependent variable from the model. The ANN used was multilayer feed forward network with back propagation of error for efficient learning. Our ANN models (with R2 = 0.68 at ten thousand iterations, and R2 = 0.71 one million iterations) performed better than traditional multivariate linear regression (MLR) models (R2 = 0.66) indicating that the ANN models were able to more accurately capture how the variations in input variables explained the variations in body weight. It is concluded that ANN models are more powerful than MLR models in predicting animals’ body weight. Nonetheless, we recognize that fitting an ANN model requires more computation resources than fitting a tradition MLR model but the benefits of its accuracy outweigh any demerit from the associated computation overhead.
Cite this paper: Salawu, E. , Abdulraheem, M. , Shoyombo, A. , Adepeju, A. , Davies, S. , Akinsola, O. and Nwagu, B. (2014) Using Artificial Neural Network to Predict Body Weights of Rabbits. Open Journal of Animal Sciences, 4, 182-186. doi: 10.4236/ojas.2014.44023.

[1]   Myers, J.L., Well, A.D. and Lorch, R.F. (2010) Research Design and Statistical Analysis. Routledge, London.

[2]   Ay, N., Flack, J. and Krakauer D.C. (2007) Robustness and Complexity Co-Constructed in Multimodal Signaling Networks. Philosophical Transactions of the Royal Society B, 362, 441-447.

[3]   Gandhi, R.S., Raja, T.V., Ruhil, A.P. and Kumar, A. (2010). Artificial Neural Network versus Multiple Regression Analysis for Prediction of Lifetime Milk Production in Sahiwal Cattle. Journal of Applied Animal Research, 38, 233-237.

[4]   Hill, T., Marquez, L., O’Connor, M. and Remus, W. (1994) Artificial Neural Network Models for Forecasting and Decision Making. International Journal of Forecasting, 10, 5-15.

[5]   Tadeusiewicz, R. (1993) Neural Networks. AOW, Warsaw.

[6]   Teguia, A., Ngandjou, H.M., Defang, S. and Tchoumboue, T. (2008) Study of the Live Body Weight and Body Characteristics of the African Muscovy Duck (Cairina moschata). Tropical Animal Health Production, 40, 5-10.

[7]   Ruhil, A.P, Raja, T.V and Gandhi, R.S. (2013) Preliminary Study on Prediction of Body Weight from Morphological Measurements of Goats through ANN Model. Journal of the Indian Society of Agricultural Statistics, 67, 51-58.