Predicting chick body mass by artificial intelligence-based models

Patricia Ferreira Ponciano Ferraz,Alessandro Torres Campos,Yamid Fabián Hernández Julio,Gregory Murad Reis,Jaqueline De Oliveira Castro,Tadayuki Yanagi Junior,Richard Stephen Gates

doi:10.1590/s0100-204x2014000700009

Patricia Ferreira Ponciano Ferraz, Alessandro Torres Campos + Show 5 more

Open Access

https://doi.org/10.1590/s0100-204x2014000700009

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Abstract

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.

Highlights

The poultry industry is facing several challenges to its sustained productivity and profitability
For the artificial neural networks (ANNs), the best network architecture was obtained with a hundred hidden neurons in the intermediate layer and in each trained ANN and an output layer consisted of only one neuron (BM)
The achieved mean square error (MSE) values showed that ANN can adequately predict the output variable

Summary

Introduction

The poultry industry is facing several challenges to its sustained productivity and profitability. Among these challenges are environmental conditions, diseases, economic pressure, feed availability and other ones. Weather is tropical in the most part of Brazil, which favors the grown of chicks in the country, broiler houses are opened and slightly thermally isolated. This makes it difficult to maintain the proper thermal environment within the facilities. Damages occur because broiler growth rate is sensitive to extreme environmental temperatures (Zhang et al, 2011)

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