Use of neural networks to detect minor and major pathogens that cause bovine mastitis

Abstract

The objectives of this research were to test the potential of unsupervised (USNN) and supervised neural network (SNN) models for detecting major and minor mastitis pathogens based on changes in milk parameters. A data set of 4,852 quarter milk samples with records for milk parameters and bacteriological status was used to train and validate the models by classifying milk samples into 3 different bacteriological states: not infected, intramammary infection (IMI) by minor pathogens, and IMI by major pathogens. Sensitivity of the USNN model was 97% for detecting noninfected quarters, 89% for minor pathogen IMI, and 80% for major pathogen IMI. Specificities of USNN models were close to 99% for all bacteriological states. The sensitivity of SNN models was affected by the ratio of infected to noninfected cases in the data set. As the ratio of infected to healthy cases increased from 1:1 to 1:10, detection accuracy for noninfected quarters increased from 82 to 98% but that for minor pathogen IMI decreased from 86 to 44%. The sensitivity for major pathogen IMI was 20% when the ratio was 1:1, but ranged from 20 to 40% when different ratios were tested. The SNN models indicated that somatic cell score and electrical resistance index had the most discriminating power. It was concluded that both USNN and SNN models were able to effectively differentiate between noninfected quarters and those infected by minor mastitis pathogens, and that the USNN model had a better agreement with results obtained from conventional microbiological methods. These types of models can be used in in-line milking systems to detect the infection status of a quarter and provide the farmer with diagnosing options for managing mastitis.

Effects of pathogen-specific clinical mastitis on probability of conception in Holstein dairy cows

“…Artificial inseminations having 2 or more CM episodes in this 12-wk period around an AI were not included in the analysis (Hertl et al, 2010), as it would not be possible to determine which CM case was contributing to the probability of conception associated with a particular AI. When 2 pathogens occurred in one CM case (e.g., cow number 4 in Table 1), 1 pathogen was chosen as the leading cause according to their expected severity of effects in the cow (Hassan et al, 2009). Table 1 shows how the CM variables were coded for 4 example cows in the data set.…”

Comparison of artificial neural network and K-means for clustering dairy cattle

“…Livestock classifications done by ANNs are quite common. Hassan et al (2009) successfully used a neural network model for the detection of mastitis in their study. In a similar study, Yang et al (2000) used ANNs in the prediction of milk production traits of cattle with clinical mastitis.…”

“…In animal science, ANNs have been successfully applied to various areas, such as diagnosis of diseases, such as mastitis and lameness (Yang et al, 1999;Cavero et al, 2008;Sun, 2008;Hassan et al, 2009;Roush et al, 2001); the prediction of forward-looking traits (Grzesiak et al, 2003;Salehi et al, 1998;Sanzogni and Kerr, 2001;Kominakis et al, 2002;Hosseinia et al, 2007;Görgülü, 2012); animal breeding studies (Shahinfar et al, 2012;Salehi et al, 1997;Grzesiak et al, 2010); the prediction of the nutrient content in manure (Chen et al, 2008(Chen et al, , 2009; and oestrus detection (Krieter et al, 2006).…”

Efektivitas Ekstrak Daun Babadotan Sebagai Green Antiseptic untuk Pencelup Puting Sapi Perah

“…Beberapa peneliti sebelum nya (De Vliegher et al, 2005., Halasa et al, 2007., Awale et al, 2012., Bhutto et al, 2012 melaporkan bahwa mastitis subklinis dapat menurunkan produksi susu sapi perah jika tidak ada penanganan. Hassan et al (2009)…”