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

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Title Mastitis detection in Murrah buffaloes with intelligent models based upon electro-chemical and quality parameters of milk
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Creator Not Available
 
Subject Connectionist models, Dairy, Error back propagation, Mastitis, Murrah buffaloes.
 
Description Not Available
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.
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Date 2021-08-25T11:07:57Z
2021-08-25T11:07:57Z
2016-03-29
 
Type Research Paper
 
Identifier Not Available
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http://krishi.icar.gov.in/jspui/handle/123456789/60554
 
Language English
 
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Publisher Not Available