Estimation of bull live weight through thermographically measured body dimensions
Computers and Electronics in Agriculture
Learning to Predict One or More Ranks in Ordinal Regression Tasks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Computers and Electronics in Agriculture
A kernel based method for discovering market segments in beef meat
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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In this paper we present a function to predict the carcass weight for beef cattle. The function uses a few zoometric measurements of the animals taken days before the slaughter. For this purpose we have used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). We report a case study done with a set of 390 measurements of 144 animals taken from 2 to 222days in advance of the slaughter. We used animals of the breed Asturiana de los Valles, a specialized beef breed from the North of Spain. The results obtained show that it is possible to predict carcass weights 150days before the slaughter day with an average absolute error of 4.27% of the true value. The prediction function is a polynomial of degree 3 that uses five lengths and the estimation of the round profile of the animals.