Analysing serial data for mastitis detection by means of local regression

Cavero, D.; Tölle, K.-H.; Rave, Gerhard; Buxadé, C.; Krieter, Joachim

doi:10.1016/j.livsci.2006.10.006

Cited by 35 publications

(43 citation statements)

References 19 publications

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“…Other published results obtained higher SENS with low SPEC (Zecconi et al 2004;Cavero et al 2008). High SENS and SPEC have been published for clinical and subclinical mastitis in cows using the increases above moving average (Mele et al 2001;Cavero et al 2007), Tracking Signal Method (De Mol et al 1999Mele et al 2001) or Fuzzy Models (Cavero et al 2006) that combine one or more variables (for example, temperature, flow or yield) with EC.…”

Section: Resultsmentioning

confidence: 98%

“…Most studied methods for cow mastitis detection using EC are based on processing data from EC sensors located at short milk tube or claw (also at gland level) and applying algorithms that consider the comparison of gland EC with the moving average of previous milkings (Lansberger et al 1994;Mele et al 2001;Biggadike et al 2002;Zecconi et al 2004;Cavero et al 2007; Kamphuis et al 2008a) and the comparison of EC of collateral glands (Maatje et al 1992(Maatje et al , 1997Lien et al 2005). In all these studies, specificity (SPEC) was around 90%, but sensitivity (SENS) was lower (different results were obtained depending on the study and type of mastitis: clinical, subclinical or somatic cell count (SCC) increases, from 25 to 89%).…”

Section: Introductionmentioning

confidence: 99%

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Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Romero¹,

Reinemann²,

Alejandro³

et al. 2014

CZECH J ANIM SCI

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Milk electrical conductivity is employed for mastitis detection in cows due to its automation, low cost, and infection detectability at early stage. Nevertheless, the number of publications about its use in dairy goats is scarce. The aim of this study was to check and compare the detectability of goat mastitis (sensitivity and specificity) using different algorithms, constructed with individual daily conductivity data from glands, in order to improve the know how about the potential of this variable for goat mastitis detection. A total of 18 goats (8 primiparous and 10 multiparous) free of mastitis were used, and gland milk conductivity was daily monitored. After 16 days of monitoring, some unfavourable situations for gland health were simulated in order to increase the cases of infection. Once infection was established (9 goats and 12 glands got infected), the experiment continued for further 16 days. A total of 19 different algorithms that employed conductivity data from gland were designed; they were tested using gland milk conductivity (EC) and ratio of EC of collateral glands in the same goat (RAT EC ). The algorithms were tested in all the animals and intramammary infection detection ability characteristics (sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), and negative predictive value (NPV)) were recorded. All clinical cases were detected (n = 2, 100% SENS) with all the algorithms. Best global SENS (clinical and subclinical, 33.3-58.3%) and SPEC (77.8-100%) were similar to results reported in previous studies in cows, and obtained with algorithms ARIMA and Rule 1 (3 standard deviations of data). The best algorithms to use in mastitis detection depend on the prevalence and type of mastitis. EC ARIMA and Rule 1 algorithms detected the most severe cases on-line and quickly, with a low proportion of false positives.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Romero¹,

Reinemann²,

Alejandro³

et al. 2014

CZECH J ANIM SCI

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“…genetic selection for maximum milk yield) also play an important role in its etiology [4][5][6]. Mastitis leads to many adverse consequences including reduced milk yield, changes in milk quality which render it unsuitable for sale, shortened productive life of the cow and lower immunity to other diseases, premature culling, and increased labor, diagnosis, veterinary and medicine costs [7][8][9][10][11]. Considerable fi nancial savings can be made by preventing mastitis and treating infected animals effectively.…”

Section: Introductionmentioning

confidence: 99%

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

et al. 2016

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The aim of the present study was to: 1) check whether it would be possible to detect cows susceptible to mastitis at an early stage of their utilization based on selected genotypes and basic production traits in the fi rst three lactations using ensemble data mining methods (boosted classifi cation tress -BT and random forest -RF), 2) fi nd out whether the inclusion of additional production variables for subsequent lactations will improve detection performance of the models, 3) identify the most signifi cant predictors of susceptibility to mastitis, and 4) compare the results obtained by using BT and RF with those for the more traditional generalized linear model (GLZ). A total of 801 records for Polish Holstein-Friesian Black-and-White cows were analyzed. The maximum sensitivity, specifi city and accuracy of the test set were 72.13%, 39.73%, 55.90% (BT), 86.89%, 17.81%, 59.49% (RF) and 90.16%, 8.22%, 58.97% (GLZ), respectively. Inclusion of additional variables did not have a signifi cant effect on the model performance. The most signifi cant predictors of susceptibility to mastitis were: milk yield, days in milk, sire's rank, percentage of Holstein-Friesian genes, whereas calving season and genotypes (lactoferrin, tumor necrosis factor alpha, lysozyme and defensins) were ranked much lower. The applied models (both data mining ones and GLZ) showed low accuracy in detecting cows susceptible to mastitis and therefore some other more discriminating predictors should be used in future research.

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“…Inflamed mammary glands, also known as bovine mastitis, lead to considerable growth in the number of such cells. This biologic process causes economic losses to the dairy industry, once it affects the milk quality (Cavero et al, 2007). The National Mastitis Council (Bramely et al, 1996) estimated annual losses per cow in the U.S. of US$ 185.00 due to mastitis, and a total annual expenditure of US$ 1.8 billion.…”

Section: Introductionmentioning

confidence: 99%

“…Systems like these, together with artificial intelligence algorithms (AI), provide data that allow early detection of mastitis. For instance, Cavero et al (2007) and Miekley et al (2012) implemented a detection system based on univariate indicator variables, whereas Kamphuis et al (2010) applied decision trees to the problem. Cavero et al (2008) and Ankinakatte et al (2013) have also developed systems based on neural networks, whereas De Mol and Woldt (2001), Cavero et al (2006) and Kramer et al (2009) devise a system based on fuzzy logic, and finally, Miekley et al (2013) are vulnerable to noise, which has a considerable influence on the characteristics of the signal to be processed and the detection technique to be applied (Kamphuis et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A robust segmentation method for counting bovine milk somatic cells in microscope slide images

Melo

Gomes

Baccili

et al. 2015

Computers and Electronics in Agriculture

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Analysing serial data for mastitis detection by means of local regression

Cited by 35 publications

References 19 publications

Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Goat mastitis detection using daily records of milk conductivity: comparative results of different algorithms

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

A robust segmentation method for counting bovine milk somatic cells in microscope slide images

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