Title :
Multi-meteorological factors-based neural network model for broiler growth performance prediction
Author :
Huang, Peijie ; Xiao, Meiyan ; Lin, Piyuan ; Yan, Shangwei
Author_Institution :
Coll. of Inf., South China Agric. Univ., Guangzhou, China
Abstract :
The purpose of this study is to investigate the prediction models for broiler growth performance. In this paper, a multi-meteorological factors-based neural network model (MMFNN) is proposed. We discuss the meteorological factors selection and the construction of MMFNN in detail. The influences of both air temperature and relative humidity to the rate for sale is taken for example to evaluate our approach. We use the broiler growth dataset of the most famous poultry raising company in China to evaluate our approach and the results show the effectiveness of our approach.
Keywords :
farming; humidity; meteorology; neural nets; China; broiler growth dataset; broiler growth performance prediction; multimeteorological factors-based neural network model; poultry breeding companies; poultry raising company; relative humidity; Bioinformatics; Biological system modeling; Feeds; Humidity; Marketing and sales; Meteorological factors; Neural networks; Predictive models; Regression analysis; Temperature distribution; bioinformatics; broiler growth performance; multi-meteorological factors; neural network;
Conference_Titel :
Bioinformatics and Biomedical Technology (ICBBT), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6775-4
DOI :
10.1109/ICBBT.2010.5478928
