Assigning pigs to uniform target weight groups using machine learning A. Alsahaf1, G. Azzopardi1, B. Ducro2, R. F. Veerkamp2, N. Petkov1 1 University of Groningen, Johann Bernoulli Institute of Mathematics and Computer Science, P.O. Box 407, 9700 AK Groningen, The Netherlands 2 Wageningen University & Research, P.O. Box 338, 6708PB Wageningen, The Netherlands A standard practice at pig farms is to assign finisher pigs to groups based on their live weight measurements or based on visual inspection of their sizes. As an alternative, we used machine learning classification, namely the random forest algorithm, for assigning finisher pigs to groups for the purpose of increasing body weight uniformity in each group. Instead of relying solely on weight measurements, random forest enabled us to combine weight measurements with other phenotypes and genetic data (in the form of EBV’s). We found that using random forest with the combination of phenotypic and genetic data achieves the lowest classification error (0.3409) in 10-fold cross-validation, followed by random forest with phenotypic and genetic data separately (0.3460 and 0.4591), then standard assignment based on birth weight (0.5611), and finally standard assignment based on the weight at the start of the finishing phase (0.7015). Keywords: machine learning, random forest, pig breeding

Ahmad Alsahaf, George Azzopardi, Bart Ducro, Roel F Veerkamp, Nicolai Petkov

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Species - Porcine 1, , 112, 2018
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