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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: bozidar.milosevic@pr.ac.rs
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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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References

AHMAD, H.A. (2009) Poultry growth modelling using neural networks and simulated data Poultry growth modelling using neural networks and simulated data. The Journal of Applied Poultry Research 18 (3): 440-446.Google Scholar
AMRAEI, S., MEHDIZADEH, S. and SALARI, S. (2016) Broiler weight estimation based on machine vision and artificial neural network. British Poultry Science 58 (2): 200-205.Google Scholar
AYBAR, R.A., JIMÉNEZ, F.S., CORNEJO, B.L., CASANOVA, M.C., SANZ, J.J., SALVADOR, G.P. and SALCEDO, S.S. (2016) A novel Grouping Genetic Algorithm-Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs. Solar Energy 132: 129-142.Google Scholar
BARBOZA, F., KIMURA, H. and ALTMAN, E. (2017) Machine learning models and bankruptcy prediction. Expert Systems With Applications 83: 405-417.Google Scholar
BREIMAN, L. (1996) Bagging Predictors. Machine Learning 24: 123-140.Google Scholar
BREIMAN, L. (2001) Random Forests. Machine Learning 45: 5-32.Google Scholar
CAMPOS, O.F., CUNHA, D.N.F.V., PEREIRA, J.C., JUNQUEIRA, M.M., MARTUSTELLO, J.A., PIRES, M.F.A. and LIZIEIRE, R.S. (2004) Utilização de diferentes abrigos para bezerros de rebanhos leiteiros em condições tropicais durante a época das águas: temperatura retal, frequência respiratória e consumo de água. Proceedings of the XLI Reunião Anual da Sociedade Brasileira de Zootecnia, Brazil, pp. 1-5.Google Scholar
CIHAN, P., GÖKÇE, E. and KALIPSIZ, O. (2017) A review of machine learning applications in veterinary field. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 23 (4): 673-680.Google Scholar
CRAMER, S., KAMPOURIDIS, M., FREITAS, A.A. and ALEXANDRIDIS, A.K. (2017) An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems With Applications 85: 169-181.Google Scholar
CRAVEN, B.D. and ISLAM, S.M.N. (2011) Ordinary least-squares regression, in: MOUTINHO, L. & HUTCHESON, G.D. (Eds) The SAGE Dictionary of Quantitative Management Research, pp. 224-228 (SAGE Publications).Google Scholar
DEMMERS, G.M., CAO, Y., GAUSS, S., LOWE, C.J., PARSONS, D.J. and WATHES, M.C. (2018) Neural predictive control of broiler chicken and pig growth. Biosystems Engineering 173: 134-142.Google Scholar
DEMMERS, T.G.M., CAO, Y., GAUSS, S., LOWE, J.C., PARSONS, D.J. and WATHES, C.M. (2010) Neural Predictive Control of Broiler Chicken Growth. IFAC Proceedings Volumes, Volume 43, Issue 6, Pages 311-316.Google Scholar
FERRAZ, P.F.P., JUNIOR, T.Y., JULIO, Y.F.H., CASTRO, J.O., GATES, R.S., REIS, G.M. and CAMPOS, A.T. (2014) Predicting chick body mass by artificial intelligence-based models. Pesquisa Agropecuária Brasileira 49 (7): 559-568.Google Scholar
FRIEDMAN, J.H. (1991) Multivariate Adaptive Regression Splines. The Annals of Statistics 19: 1-67.Google Scholar
GHAZANFARI, S. (2014) Application of Linear Regression and Artificial Neural Network for Broiler Chicken Growth Performance Prediction. Iranian Journal of Applied Animal Science 4 (2): 411-416.Google Scholar
GOODFELLOW, I., BENGIO, Y. and COURVILLE, A. (2016) Deep Learning, MIT Press: Cambridge, MA, USA, pp. 216-261.Google Scholar
HEPWORTH, P.J., NEFEDOV, A.V., MUCHNICK, I.B. and MORGAN, K.L. (2012) Broiler chickens can benefit from machine learning: Support vector machine analysis of observational epidemiological data. Journal of The Royal Society Interface 9: 1934-1942.Google Scholar
JOHANSEN, V., BENDTSEN, S.D., JENSEN, R.J. and JESPER, M.M. (2017) Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models. IFAC-PapersOnLine 50: 5398-5403.Google Scholar
JOVIĆ, S., MAKSIMOVIĆ, G. and JOVOVIĆ, D. (2016) Appraisal of natural resources rents and economic development. Resources Policy 50: 289-291.Google Scholar
KANG, J., SCHWARTZ, R., FLICKINGER, J. and BERIWAL, S. (2015) Machine learning approaches for predicting radiation therapy outcomes: A clinician's perspective. International Journal of Radiation Oncology, Biology, Physics 93: 1127-1135.Google Scholar
KIRBY, Y.K., MCNEW, R.W., KIRBY, J.D. and WIDEMAN, R.F. (1997) Evaluation of logistic versus linear regression models for predicting pulmonary hypertension syndrome (ascites) using cold exposure or pulmonary artery clamp models in broilers. Poultry Science 76: 392-399.Google Scholar
LOPES, A.Z., JUNIOR, T.Y., LACERDA, W.S. and RABELO, D. (2014) Predicting Rectal Temperature of Broiler Chickens with Artificial Neural Network. International Journal of Engineering & Technology IJET-IJENS 14 (05): 29-34.Google Scholar
LÓPEZ, C.X.A., NACHTIGALL, F.M., OLATE, V.R., ARAYA, M., OYANEDEL, S., DIAZ, V., JAKOB, E., RÍOS, M.M. and SANTOS, L.S. (2017) Fast detection of pathogens in salmon farming industry. Aquaculture 470: 17-24.Google Scholar
MAKSIMOVIĆ, G., JOVIĆ, S., JOVANOVIĆ, R. and ANICIC, O. (2016) Management of health care expenditure by soft computing methodology. Physica A: Statistical Mechanics and its Applications 465: 370-373.Google Scholar
MAKSIMOVIĆ, G., JOVIĆ, S., JOVOVIĆ, D. and JOVOVIĆ, M. (2017) Analyses of Economic Development Based on Different Factors. Computational Economics: 10.1007/s10614-017-9786-1.Google Scholar
MEDEIROS, C.M., BAÊTA, F.C., OLIVEIRA, F.M., TINÔCO, I.F.F., ALBINO, L.F.T. and CECON, P.R. (2005) Efeitos da temperatura, umidade relativa e velocidade do ar em frangos de corte. Engenharia Agrícola 13 (4): 277-286.Google Scholar
MOHARRERY, A. and KARGAR, A. (2007) Artificial Neural Network for prediction of plasma hormones, liver enzymes and performance in broilers. Journal of Animal and Feed Sciences 16: 293-304.Google Scholar
PEARL, J. (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kauffmann San Mateo 88: 552.Google Scholar
QUINLAN, J.R. (1992) Learning with continuous classes. Machine Learning 92: 343-348.Google Scholar
RHEE, J. and IM, J. (2017) Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Agricultural and Forest Meteorology 237-238: 105-122.Google Scholar
ROUSH, W.B., CRAVENER, T.L., KIRBY, Y.K. and WIDEMAN, R.F. (1997) Probabilistic neural network prediction of ascites in broilers based on minimally invasive physiological factors. Poultry Science 76: 1513-1516.Google Scholar
ROUSH, W.B., DOZIER, W.A. and BRANTON, S.L. (2006) Comparison of Gompertz and neural Network Models of Broiler Growth. Poultry Science 85 (4): 794-797.Google Scholar
ROUSH, W.B., KIRBY, Y.K., CRAVENER, T.L. and WIDEMAN, R.F. (1996) Artificial neural network predictions of ascites in broilers. Poultry Science 75: 1479-1487.Google Scholar
SALAKHUTDINOV, R. and HINTON, G. (2009) Deep Boltzmann Machines. AIStats 1: 448-455.Google Scholar
SCHAPIRE, R.E. (1999) A brief introduction to boosting. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Volume 2, pp. 1401-1406.Google Scholar
SMOLA, A. (1996) Regression Estimation with Support Vector Learning Machines. Master's Thesis, The Technical University of Munich.Google Scholar
SPIERS, D.E., SPAIN, J.N., SAMPSON, J.D. and RHOADS, R.P. (2004) Use of physiological parameters to predict milk yield and feed intake in heatstressed dairy cows. Journal of Thermal Biology 29 (7-8): 759-764.Google Scholar
SUYKENS, J.A.K., VAN GESTEL, T., DE BRABANTER, J., DE MOOR, B. and VANDEWALLE, J. (2002) Least Squares Support Vector Machines, World Scientific: Singapore.Google Scholar
VAPNIK, V. (1995) Support vector machine. Machine Learning 20: 273-297.Google Scholar
WANG, L., SUN, C., LI, W., JI, Z., ZHANG, X., WANG, Y., LEI, P. and YANG, X. (2017) Establishment of broiler quality estimation model based on depth image and BP neural network. Transactions of the Chinese Society of Agricultural Engineering 33 (13): 199-205.Google Scholar
WIDEMAN, R.F. (1988) Ascites in poultry. Monsanto Nutrition Update 6 (2): 1-7.Google Scholar
WIDEMAN, R.F. and BOTTJE, W.G. (1993) Current understanding of the ascites syndrome and future research directions. Nutrition and Technical Symposium Proceedings, pp. 1-20.Google Scholar
ZHANG, B., HE, X., OUYANG, F., GU, D., DONG, Y., ZHANG, L., MO, X., HUANG, W., TIAN, J. and ZHANG, S. (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Letters 403: 21-27.Google Scholar
ZHAO, Y., LI, J. and YU, L. (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Economics 66: 9-16.Google Scholar
ZHOU, C., LIN, K., XU, D., CHEN, L., GUO, Q., SUN, C. and YANG, X. (2018) Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Computers and Electronics in Agriculture 146: 114-124.Google Scholar
ZHUANG, X., BI, M., GUO, J., WU, S. and ZHANG, T. (2018) Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture 144: 102-113.Google Scholar