Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

Mammadova, Nazira M.; Keskin, İsmail

doi:10.1155/2013/603897

Cited by 34 publications

(19 citation statements)

References 23 publications

(18 reference statements)

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“…When such features were included in the training of the model, the AUC increased to 0.81, showing an improved performance of the RF approach on this dataset (Hidalgo et al, 2018). Mammadova and Keskin (2013) used SVMs to ascertain the presence of subclinical and clinical mastitis in dairy cattle. They used 346 (61 mastitis cases) measurements of milk yield, electrical conductivity, average milking duration and somatic cell count collected from February 2010 to April 2011 for 170 Holstein Friesian dairy cows.…”

Section: Prediction Of Disease Phenotypesmentioning

confidence: 99%

A review of traditional and machine learning methods applied to animal breeding

Nayeri

Sargolzaei

Tulpan

2019

Anim. Health. Res. Rev.

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The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

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Section: Prediction Of Disease Phenotypesmentioning

confidence: 99%

A review of traditional and machine learning methods applied to animal breeding

Nayeri

Sargolzaei

Tulpan

2019

Anim. Health. Res. Rev.

View full text Add to dashboard Cite

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“…Thus, EC of milk is now being used increasingly in the dairy industry to detect mastitis (Mammadova and Keskin, 2013). Also, it is a well-known fact that mastitis is associated with increased conductivity of udder tissue and changes in ionic composition of milk.…”

Section: Introductionmentioning

confidence: 99%

Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters

Panchal

Sawhney

Sharma

et al. 2016

Computers and Electronics in Agriculture

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“…Electrical conductivity between the RF and the RR quarters of the third and more lactating cows was 1.06 mS/ cm higher in comparison to the adequate quarters of the first lactation cows (P<0.05). It was reported that the electrical conductivity of 5.5 mS/cm in milk was a threshold for the subclinical mastitis (Mammadova et al, 2013). Other researches reported that the electrical conductivity of milk ranged from 4.6 to 5.8 mS/cm in samples with somatic cell counts below 200.000/mL and variation in electrical conductivity of milk could be accepted as one of the key parameters in the milking system for health monitoring system of dairy cows (Juozaitienė et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Quarter milking parameters by lactation in dairy cows

et al. 2019

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The study was conducted on 229 dairy cows in Lithuania. DeLaval milking robots were used for cows’ milking. Milk yield (kg), milking duration (min), milk flow (kg/min), peak flow rates (kg/min) by cow lactation in separate udder quarters were analyzed. The statistical analysis of the research data was performed using the data collection and analysis program package Statistical Package for Social Science 22 for Windows, Kolmogorov-Smirnov test and One-Factor Dispersion Analysis (ANOVA) model. Distribution of the udder quarters is very important in the cow milking process. Milk yield from cow front udder quarters was about 4.6 kg (42.2 %) and from rear udder quarters 6.32 kg (57.8 %) (P<0.05). The milk content in front quarters was significantly lower than in the rear udder quarters in all dairy cows (P<0.05). Cows of the first lactation showed lower milk production per milking in all udder quarters when compared to other lactation cows (P<0.05). The duration of milking of cows in third and more lactation in front quarters was shorter than that of the first lactation cows (P<0.005). Along the increase in the number of cows’ lactations, the milking time in the front quarters decreased. The average milk flow and the peak flow in the first lactation cows were found to be lower than that in older cows (P<0.05).

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Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

Cited by 34 publications

References 23 publications

A review of traditional and machine learning methods applied to animal breeding

A review of traditional and machine learning methods applied to animal breeding

Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters

Quarter milking parameters by lactation in dairy cows

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