Predicting chick body mass by artificial intelligence-based models
| Autor: | Patricia Ferreira Ponciano Ferraz, Tadayuki Yanagi Junior, Yamid Fabián Hernández Julio, Jaqueline de Oliveira Castro, Richard Stephen Gates, Gregory Murad Reis, Alessandro Torres Campos |
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| Jazyk: | English<br />Spanish; Castilian<br />Portuguese |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Pesquisa Agropecuária Brasileira, Vol 49, Iss 7, Pp 559-568 (2014) |
| Druh dokumentu: | article |
| ISSN: | 1678-3921 0100-204X |
| DOI: | 10.1590/S0100-204X2014000700009 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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