Artificial Neural Network (ANN) Reliability Analysis of Poultry Bird Production Through Nigerian Made Incubator

I.O. Adewumi¹

1. Department of Agricultural and Bio-Environmental Engineering, Federal College of Agriculture, Ibadan, Nigeria.

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.1 , pp.25-31, Mar-2020

Online published on Apr 10, 2020

Copyright © I.O. Adewumi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

142 Views    203 Downloads    80 Downloads

Abstract :
Promotion of local content has been focus of Nigeria government and world at larg. But it is important to be sure about the standard of the content produced. This will avoid mixing quality with quantity. These research work specially focus on reliability analysis of poultry bird production through Nigerian made incubator. The researcher evaluate an incubator at Arise Gods Farm, Odo-Ona Elewe Ibadan. The Artificial Neural Network (ANN) model was used to model time-series data and future forecast for a locally fabricated poultry egg incubator with the data made available between 36 months of use by a local poultry farmer with three of our researchers on field within the period. The ANN model was developed with 70% of the data for training, 20% for testing and 10% for model validation. Based on this model, forecasting was carried out on hatching process and also the failure rate of the machine. The input parameters include, months, numbers of eggs loaded in the incubator, number of birds produced, hatchability rate, failure rate, temperature and relative humidity. The result of the statistical analysis shows the effect of input parameters which include the number of eggs loaded, hatchability, failure rate, production period, had no significant difference at R² = 1. While the month, temperature and relative humidity point significantly (p < 0.005) influence on the machine reliability.

Key-Words / Index Term :
ANN, analysis, artificial intelligence, incubator, Nigerian made, poultry

References :
[1] T. A Adegbulugbe., A. O. Atere, O. G Fasanmi, “Development of Automatic Egg Incubator,” International Journal of Scientific & Engineering Research, Vol.4, Issue 9, pp.914, 2011.
[2] N.A. French, “Modelling incubation temperature: The effect of incubator design, embryonic development and egg size,” Poultry Science Journal,Vol.76, Issue.5, pp.124-133, 1997.
[3] G. A. Ogunwande, E. O. Akinola and A. R Lana, “Development Of A Biogas Powered Poultry Egg Incubator,” Ife Journal of Science, Vol.17, Issue.1, pp.219, 2015.
[4] J. C Phillip, “Poultry Extension Specialist,” Journal of Animal and Poultry Sciences. Virginal Cooperative Extension, Vol.6, pp.5, 2016.
[5] C.N Jaime, “Artificial Neural Networks in Mexican Agriculture: An Overview,” International Journal of Research in Agriculture and Forestry, Vol.5, Issue.7, pp.3, 2018.
[6] S.D Snehal and V.R Sandeep, “Agricultural Crop Yeild Prediction Using Artificial Neural Network Approach,” International Journal of Innovative Research in Electrical, Electronics and Instrumentation and Control Engineering, Vol.2, Issue.1, pp.652, 2014.
[7] S Jiri, Vladimir K, and T Oldrich, “Agricultural Data Prediction by Means of Neural Network,” International Journal of Agricultural Economics- Czech Republic, Vol.57, Issue.7, pp.356, 2014.
[8] AM Negm, AA Ibrahim, GM Abdel-Aal, AA El-Saiad, “Modeling of characteristics of hydraulic jumps at asymmetric sudden expansions and symmetric divergence using neural networks,” Scientific Bulletin, Faculty of Engineering, Ain Shams University, Faculty of Engineering, Cairo, Egypt, Vol.6, Issue.9, pp.22, 2001.