Live Pig-Weight Learning and Prediction Method Based on a Multilayer RBF Network

Abstract

The live weight of pigs has always been an important reference index for growth monitoring and the health status of breeding pigs. An accurate weight acquisition of breeding pigs is the key to guide the scientific feeding of breeding pigs and improve economic benefits. Compared with the traditional contact measurement method, the non-contact weighing method of live pigs can greatly reduce human–pig contact and measurement errors. In this paper, a deep neural network is constructed which can automatically and accurately predict the weight of live pigs by measuring multiple body parameters. Because of the good generalization ability of the radial basis function (RBF) neural network and the better fitting ability of multilayer network than the traditional single-layer network, this paper introduces a full-connection model in the middle layer, connects multiple RBF layers, builds a multilayer RBF network, and invents the automatic learning method of pig weight based on the network. In this method, the body length, body height, body width, and five other body parameters are input, after normalization, into the multilayer RBF network model for training, and resultingly the network gives a predicted weight. Among our 4721 live pigs, there are 2452 sows and 2269 boars, among which 2000 samples of sows are randomly selected as training sets and 452 samples as test sets; 1930 samples of boars are taken as training sets and 339 samples as test sets. The test shows that the performance of the network structure is as follows: R2 is 0.63, MAE is 1.85, RMSE is 5.74, and MAPE is 1.68.