Application of soft computing models for prediction of subclinical mastitis in indigenous breed of dairy cattle in India
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Authors
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Indu Panchal
Assistant Professor
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Sumit Mahajan
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Sharanagouda B
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Kanta Yadav
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I K Sawhney
Keywords:
Adaptive Neuro Fuzzy Inference System, Mastitis, Murrah buffaloes, Sahiwal cows, Soft computing models, Subclinical mastitisAbstract
Mastitis is an important problem in dairy cattle. Soft computing models i.e. Adaptive Neuro Fuzzy Inference System (ANFIS) can be possible way out for detecting this disease. Therefore, the present study was undertaken for predicting the subclinical mastitis in indigenous breed such as Sahiwal cows and Murrah buffaloes. The selected eight parameters for study were Milk pH, electrical conductivity, temperature (udder, milk and skin), milk yield, dielectric constant and milk somatic cell counts. Animals were judged healthy and infected as per milk somatic cell counts. Accordingly, animals were classified into three categories, i.e., healthy, subclinical and clinical mastitis animals. Data generated were utilized for developing ANFIS models to identify healthy versus mastitis animals. Also, Multiple Linear Regression (MLR) models were developed for comparing classification accuracy of proposed models using Root Mean Square Error (RMSE) technique. ANFIS models were found to be superior as compared to MLR models for both the breed with RMSE 0.23 (Shahiwal cows) and 0.20 (Murrah buffaloes) as compare to MLR model 4.88 (Shahiwal cows) and 4.08 (Murrah buffaloes). Hence, it is deduced that ANFIS can be used as a suitable technique for detecting mastitis in indigenous breed of dairy cattle.
References
Alhussien MN, Dang AK (2018) Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview Vet World 11: 562–577
Barth K, Fischer R, Worstorff H (2000) Evaluation of variation in conductivity during milking to detect subclinical mastitis in cows milked by robotic system. Pages 89-96. Proceedings in International Symposiumon Robotic Milking. H. Hogeveen and A. Meijering, ed. Wageningenpers, Wageningen, The Netherlands
Bogni C, Odierno L, Raspanti, C. Giraudo J, Larriestra A, Reinoso E, Lasagno M, Ferrari M, Ducros E, Frigerioc C, Bettera S, Pellegrino MS, Frola I, Dieser S, Vissio C (2011) War against mastitis: Current concepts on controlling bovine mastitis pathogens. In: Mendez-Vilas A; Education Science against microbial pathogens: Communicating current research and technological advances. Formatex Research Center 483-494
Cavero D, Tolle KH, Buxade C, Krieter J (2006) Mastitis detection in dairy cows by application of Fuzzy Logic. Livest Sci 105: 207-213
Dang AK, Mukherjee J, Kapila S (2010) In vitro phagocytic activity of milk neutrophils during lactation cycle in Murrah buffaloes of different parity. J Anim Physiol Anim Nutr 94: 706–711
De Mol RM, Woldt WE (2001) Application of fuzzy logic in automated cow status monitoring. J Dairy Sci 84: 400–410.
Dua K (2001) Incidence, etiology and estimated economic losses due to mastitis in Punjab and in India - An update. Indian Dairyman 53: 41–48
Gaddi RM, Isloor S, Rathnamma D, Avinash B, Veeregowda BM, Bhaskar R, SugunaRao (2016) Multiplex-pcr to detect pathogens and analysis of relation of age and stage of lactation of cows to sub-clinical mastitis. J Exp Bio Agri Sci 4: S59-S68
Jacceh M (2003) Neuro-Fuzzy System with learning tolerant to imprecision. Fuzzy Sets Syst 138: 427–439
Krieter, J, Cavero D, Henze C (2007) Mastitis detection in dairy cows using neural networks. GIL Jahrestagung conference. 101:123-126
Mammadova N, 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
Mammadova N, Keskin I. (2013) Application of the support vector machine to predict subclinical mastitis in dairy cattle The Sci World J 1–9. DOI:10.1155/2013/603897.
Seegers H, Fourichon C, Beaudeau F (2003) Production effects related to mastitis economics in dairy cattle herds. Vet Res 34: 475–491
Sharma AK, Sawhney IK, Lal M (2014) Intelligent modeling and analysis of moisture sorption isotherm in milk and pearl millet based weaning food “fortified nutrimix†Drying Tech. 32: 728-741
Skrzypek R, Wojtowski J, Fahr RD (2004) Factor affecting somatic cell counts in cow bulk tank milk a case study from Poland. J Vet Med Sci 51: 127-131
Srivastava AK, Manimaran A, Prasad S (2015) Mastitis in Dairy Animals An Update, Satish Serial Publishing House, New Delhi, India
Veleva Doneva P, Draganova T, Atanassova S, Tsenkova R (2010) Detection of bacterial contamination in milk using NIR spectroscopy and two classification methods - SIMCA and Neuro – Fuzzy classifier. IFAC Proceeding. 43: 225-229
