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

Haoming Chen, Yun Liang (), Hao Huang, Qiong Huang, Wei Gu and Hao Liang
Additional contact information
Haoming Chen: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yun Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Hao Huang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Qiong Huang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Wei Gu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Hao Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2023, vol. 13, issue 2, 1-12

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.

Keywords: deep learning; live pig; weight estimation; multilayer RBF neural network; health monitoring (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/2/253/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/2/253/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:2:p:253-:d:1042457

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().