Application of soft computing models for prediction of subclinical mastitis in indigenous breed of dairy cattle in India - Panchal, Indu; Mahajan, Sumit; B, Sharanagouda; Yadav, Kanta; Sawhney, I K (Indian Journal of Dairy Science, 2020)
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.