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Machine learning application in growth and health prediction of broiler chickens

Published online by Cambridge University Press:  20 August 2019

B. MILOSEVIC Open the ORCID record for B. MILOSEVIC [Opens in a new window] ,
S. CIRIC ,
N. LALIC ,
V. MILANOVIC ,
Z. SAVIC ,
I. OMEROVIC ,
V. DOSKOVIC ,
S. DJORDJEVIC  and
L. ANDJUSIC
B. MILOSEVIC*
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
S. CIRIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
N. LALIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
V. MILANOVIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
Z. SAVIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
I. OMEROVIC
Affiliation:
State University of Novi Pazar, Serbia
V. DOSKOVIC
Affiliation:
University of Kragujevac, Faculty of Agronomy, Cacak, Serbia
S. DJORDJEVIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
L. ANDJUSIC
Affiliation:
University of Pristina, Faculty of Agriculture Kosovska Mitrovica, Serbia
*
Corresponding author: [email protected]
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Abstract

Artificial intelligence (AI) already represents a factor for increasing efficiency and productivity in many sectors, and there is a need for expanding its implementation in animal science. There is a growing demand for the development and use of smart devices at the farm level, which would generate enough data, which increases the potential for AI using machine learning algorithms and real-time analysis. Machine learning (ML) is a category of algorithm that allows software to become accurate in predicting outcomes without being explicitly programmed. The essential principle of machine learning is to construct algorithms that can receive input data and use statistical analysis to predict an output. Exploitation of machine learning approaches, by using different training inputs, derived the prediction accuracy of growth and body weight in broiler chickens that ranged from 98 to 99%. Furthermore, a neural network with an accuracy of 100% identified the presence or absence of ascites in broiler chickens, while the support vector machine (SVM) model obtained an accuracy rate of 99.5% in combination with machine vision for the recognition of healthy and bird flu-challenged chickens. Consequently, machine learning algorithms, besides accurate growth prediction of broiler chickens, can successfully contribute to health disorders prediction. It is obvious that machine learning has a great potential for application in the future. This paper analyses machine learning applications in broiler growth and health prediction, and its ability to cope with high inputs of data and non-linearity can successfully replace common methodology.

Keywords

machine learningbroilergrowthhealth
Type
Review
Information
World's Poultry Science Journal , Volume 75 , Issue 3 , September 2019 , pp. 401 - 410
Copyright
Copyright © World's Poultry Science Association 2019 

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