Original papers Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters I. Panchal a,⇑ , I.K. Sawhney a , A.K. Sharma b , A.K. Dang c a Dairy Engineering Division, ICAR – National Dairy Research Institute (Deemed University), Karnal 132001, Haryana, India b Dairy Economics, Statistics and Management Division, and Officer-in-Charge, Computer Centre, ICAR – National Dairy Research Institute (Deemed University), Karnal 132001, Haryana, India c Dairy Cattle Physiology Division, ICAR – National Dairy Research Institute (Deemed University), Karnal 132001, Haryana, India article info Article history: Received 9 December 2015 Received in revised form 20 May 2016 Accepted 14 June 2016 Keywords: Electro-chemical properties of milk Error back propagation Mastitis Milk yield Murrah buffaloes Neural network model abstract This paper describes a Neural Network (NN) model to classify healthy and mastitis Murrah buffaloes using pH, electrical conductivity, dielectric constant and yield of milk as input parameters and California Mastitis Test (CMT) score as the output parameter. The purpose of this study was to develop such a cost-effective and intelligent classification model, which would serve an alternative to the prevail- ing Somatic Cell Count (SCC) based techniques to detect mastitis in Murrah buffaloes, because the latter techniques are sophisticated, lengthy and time consuming as well as necessary instruments for carrying out the tests are not, generally, available at the grassroots level or to the small dairy holders. Accordingly, a total of 534 milk samples were collected from 100 lactating Murrah buffaloes, which were scrutinized for mastitis using CMT. The animals were classified into three categories, i.e., healthy, subclinical and clinical mastitis buffaloes and assigned CMT scores as 1, 2, and 3, respectively. The NN models were based on error back propagation learning algorithm with Bayesian regularization mechanism and various com- binations of internal parameters. The performance of NN models was compared with that of conventional Multiple Linear Regression (MLR) models also developed in this study. The classification accuracy achieved by the best NN model was 8.02 Root Mean Square percent error (%RMS) while that attained by MLR model was 26.47 %RMS. Further, for classifying healthy vs. subclinical mastitis Murrah buffaloes, sensitivity, specificity and Diagnostic Odds Ratio (DOR) with the best NN model was found to be 98%, 97.72% and 54.87, respectively, having Area under Relative Operating Characteristic (ROC) Curve (AUC) as 0.96 vis-à-vis MLR model attaining the same as 58.87%, 76.72%, and 52.26, respectively, and AUC as 0.81. In case of classifying healthy vs. clinical mastitis Murrah buffaloes, the best NN model achieved sen- sitivity, specificity and DOR as 99%, 97.28% and 57.92, respectively, with AUC as 0.98 while that with MLR model were determined as 69.23%, 78.20% and 55.46, respectively, and AUC as 0.87. Evidently, the NN model outperformed classical MLR model, in this study. Hence, it can be deduced that NN paradigm has potential to efficiently detect healthy and mastitis Murrah buffaloes on the basis of milk yield and milk quality parameters. Ó 2016 Elsevier B.V. All rights reserved. 1. Introduction Mastitis is the most costly disease in dairy animals and remains one of the main problems for the dairy-industry because it leads to reduced milk production, involves treatment cost, milk withhold- ing following treatment and premature culling (Hogeveen et al., 2011; Sawhney, 2013). It is evident from the studies conducted in the United States of America that costs associated with mastitis on dairy farms are approximately US $200 per cow/year leading to annual loss of US $2 billion for dairy industry (Bogni et al., 2011). In India, annual economic loss incurred by dairy industry on account of udder infections has been estimated to be US $908 million and out of which loss of US $655.40 million (70–80%) has been attrib- uted to subclinical mastitis (Srivastava et al., 2015). In another report from India (Anon., 2011), the annual economic loss due to mastitis has been calculated as US $1075.8 million; losses being almost same for cows (US $548 million) and buffaloes (US $528 million). Subclinical mastitis has been estimated to account for 57.93% (US $623.2 million) of the total economic loss due to mastitis. http://dx.doi.org/10.1016/j.compag.2016.06.015 0168-1699/Ó 2016 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail address: indupanchal33@gmail.com (I. Panchal). Computers and Electronics in Agriculture 127 (2016) 242–248 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag