Predicting chick body mass by artificial intelligence-based models

Pesquisa Agropecuária Brasileira

Patricia Ferreira Ponciano Ferraz¹  Tadayuki Yanagi Junior¹  Yamid Fabián Hernández Julio¹  Jaqueline De Oliveira Castro¹  Richard Stephen Gates¹  Gregory Murad Reis¹  Alessandro Torres Campos¹ 

关键词: animal welfare;    artificial neural network;    broiler;    modeling;    neuro-fuzzy network;    thermal comfort;    bem estar animal;    redes neurais artificiais;    frango;    modelagem;    redes neurais difusas;    conforto térmico;   

DOI  :  10.1590/S0100-204X2014000700009

来源: SciELO

【 摘 要 】

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

【 授权许可】

CC BY   
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