A new method to learn growth curves of beef cattle using a factorization approach

作者：Alonso Jaime^*; Diez Jorge; Luaces Oscar; Bahamonde Antonio

来源：Computers and Electronics in Agriculture, 2018, 151: 77-83.

DOI：10.1016/j.compag.2018.05.033

• Recommender systems
• Matrix factorization
• Stochastic gradient descent
• Growth curves
• Beef cattle

The evolution of cattle weight is a very important issue for beef cattle breeders. The weights of the animals of a herd are usually available at different ages and it is intended to predict the trajectory that will follow the weight of each animal. In this paper, we address this problem as a Recommender System. In this case, the users would be the animals, and the items would be the ages of weight measurements. The values of the items would be the measured weights at a given age. As in Recommender Systems the aim is to complete the valuation matrix (weights) in an individualized way (that is, adapted to each animal). A matrix factorization system is devised to learn weights using all the available characteristics of the animals. The weights thus obtained are compared with a linear regression that adequately estimates the general evolution of the herd, but not the individual evolution of each animal. To illustrate the benefits of this approach, we used a real world dataset of cattle of the breed Avilena-Negra Iberica and crossbreeding with sires of Charolais and Limousin.

• 出版日期2018-8