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

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.

Meta-analysis of Prevalence of Subclinical and Clinical Mastitis, Major Mastitis Pathogens in Dairy Cattle in India

“…Monica et al, 2016 Tamil Nadu South 3 71. Panchal et al, 2016 Haryana North 1, 2 72. Singh et al, 2016 Madhya Pradesh Central 2, 3 Note: SCM-Subclinical mastitis; CM-Clinical mastitis; MMP-Major mastitis pathogens Note: ns-Non Significant; *-Significant at P<0.05; ** -Significant at P<0.01; ***Significant at P<0.001 However, the SCM was caused by many pathogens, without any clinical signs and unobservable nature of the infection, which is leading to major economic loss to the dairy farmers in India.…”

Prediction of Frozen Semen Doses Production in Dairy Studs Using Machine Learning Algorithm

“…These characteristics facilitate the advantage of speed, efficient and error tolerant modelling (Mammadova and Keskin, 2015). ANN models are being used and tested in many fields of livestock and dairying, especially in predicting mastitis (Panchal et al, 2017), conception success (Hempstalk et al, 2015), milk yield…”

Electrical conductivity and total dissolved solid of raw milk for the detection of bovine subclinical mastitis

“…Both tests are simple, easy to use, and more cost-effective than traditional methods. Electric conductivity is a measure of milk’s resistance to the flow of electricity and is influenced by the presence and concentration of specific ions, primarily sodium, potassium, and chloride ions [ 13 , 14 ]. Similarly, TDS is a physiochemical indicator that can rapidly detect SCM by monitoring changes directly related to pathophysiological reactions, species, or geoclimatic variation.…”