Mastitis detection in Murrah buffaloes with intelligent models based upon electro-chemical and quality parameters of milk 

DOI: 10.18805/ijar.10773    | Article Id: B-3153 | Page : 922-926
Citation :- Mastitis detection in Murrah buffaloes with intelligent models based upon electro-chemical and quality parameters of milk .Indian Journal Of Animal Research.2017.(51):922-926

Indu Panchal, I.K. Sawhney, A.K Sharma, M.K. Garg and A.K. Dang

indupanchal33@gmail.com
Address :

ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.

Submitted Date : 10-12-2015
Accepted Date : 29-03-2016

Abstract

In this paper, several connectionist models have been described to detect mastitis in Murrah buffaloes using milk parameters, viz., pH, electrical conductivity, temperature (udder, milk and skin), milk somatic cells, milk yield and dielectric constant. A total of 600 milk samples were collected from 100 lactating Murrah buffaloes; which were analysed for Somatic Cell Counts in milk. Accordingly, animals were classified into three categories, i.e., healthy, subclinical mastitis and clinical mastitis animals. These basal values were utilised for developing connectionist models to identify healthy versus mastitis animals. Also, Multiple Linear Regression (MLR) models were developed for comparing classification accuracy of proposed connectionist models using Root Mean Square Error (RMSE) technique. The connectionist models were found to be superior (RMSE = 0.01) as compared to MLR models (RMSE = 4.08). Hence, it is deduced that connectionist approach could be used as a suitable technique for detecting mastitis in Murrah buffaloes.

Keywords

Connectionist models Dairy Error back propagation Mastitis Murrah buffaloes.

References

  1. Ankinakatte, S.A., Norberg, E., Lovendahl, P., Edwards, D. and Hojsgaard, S. (2013). Predicting mastitis in dairy cows using neural networks and generalized additive models: a comparison. Comput. Electron. Agr. 99: 1-6.
  2. Atil, H. and Akilli, A. (2015). Investigation of dairy cattle traits by using artificial neural networks and cluster analysis. In: Proceedings of the 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2015), Kavala, Greece, September 17-20, pp. 681-690.
  3. Cavero, D., Tolle, K.H., Buxade, C. and Krieter, J. (2008). Mastitis detection in dairy cows by application of neural networks. Livest. Sci. 114: 280-286.
  4. De, K., Mukherjee, J., Prasad, S. and Dang, A.K. (2010). Effect of different physiological stages and managemental practices on milk somatic cell counts of Murrah buffaloes. In: Proceedings of the 9th World Buffalo Congress, Buenos Aires, Argentina, April 25-28, pp. 549-551. 
  5. Hassan, K.J., Samarasinghe, S. and Lopez-Benavides, M.G. (2007). The use of neural networks to detect minor and major pathogens that cause bovine mastitis. In: Proceedings of the New Zealand Society of Animal Production, 67: 215-219.
  6. Heald, C.W., Kim, T., Sischo, W.M., Cooper, J.B. and Wolfgang, D.R. (2000). A computerized mastitis decision aid using farmbased records: An artificial neural network approach. J. Dairy Sci. 83:711-720.
  7. Lopez-Benavides, M.G., Samarasinghe, S. and Hickford, J.G.H. (2003). The use of artificial neural networks to diagnose mastitis in dairy cattle. In: Proceedings of the International Joint Conference on Neural Networks, July 20-24, 1: 582 - 585.
  8. Mammadova, N.M. and Keskin, I. (2015). Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle. Indian J. Anim. Res. 49: 671-679. www.arccjournals.com/uploads/    articles/19671679B273.pdf. 
  9. Schroeder, J.W. (2012). Mastitis control programs: Bovine mastitis and milking management. NDSU Extension Service publication: AS1129, North Dakota State University, Fargo, North Dakota. www.ag.ndsu.edu/pubs/ansci/dairy/    as1129.pdf.
  10. Srivastava, A.K., Kumaresan, A., Manimaran, A. and Prasad, S. (2015). Mastitis in dairy animals - An update. Satish Serial Publishing House, New Delhi, India. ISBN-10: 9384053066.
  11. Sun, Z., Samarasinghe, S. and Jago, J. (2010). Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks. J. Dairy Res. 77:168-75.

Global Footprints