Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk

Kamphuis, C.; Pietersma, D.; Tol, P.P.J. van der; Wiedemann, Martin; Hogeveen, H.

doi:10.1016/j.compag.2007.12.009

Cited by 45 publications

(34 citation statements)

References 12 publications

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“…1) characterized the variability or shape of sensor measurements, and they were based on comparisons with previous quarter milkings or with other quarters in the same cow milking. This finding is in line with results from Kamphuis et al (2008a). They concluded that sensor data from the electrical conductivity and the color sensors blue and green contained the most information for abnormal milk or CM classification, and that variables based on the variability or shape (e.g., the range or increase) of sensor measurement patterns could be as predictive as variables based on the level of sensor measurement patterns (e.g., the mean value).…”

Section: Discussionsupporting

confidence: 79%

“…Raw sensor data of the electrical conductivity, color, and an estimation of quarter milk yield were collected by connecting a remote computer to each of the 12 automatic milking systems. From these raw sensor measurements, 1065 potentially descriptive variables were developed using a data flow diagram (Kamphuis et al, 2008a(Kamphuis et al, , 2010a. These variables described characteristics (level, variability, and shape) of sensor measurements patterns from each quarter milking.…”

Section: Data Collectionmentioning

confidence: 99%

“…These studies showed varying predictive performances of the classification models developed, or concluded that collection of the necessary information would take too long for an appropriate prediction at cow-level. Kamphuis et al (2008a) reported a difference in sensor measurement patterns of the electrical conductivity and the colors red, green, and blue between healthy quarters and quarters infected with CM, but they did not check whether different pathogens show different sensor measurement patterns. Espada and Vijverberg (2002) expected color sensor measurements to be useful for abnormal milk detection and potentially also for pathogen prediction.…”

Section: Introductionmentioning

confidence: 99%

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Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Kamphuis

Mollenhorst

Hogeveen

2011

Computers and Electronics in Agriculture

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a b s t r a c tAutomatic milking systems produce mastitis alert lists that report cows likely to have clinical mastitis (CM). A farmer has to check these listed cows to confirm a CM case and to start an antimicrobial treatment if necessary. In order to make a more informed decision, it would be beneficial to have information about the CM causal pathogen at the same time a cow is listed on the mastitis alert list. Therefore, this study explored whether decision-tree induction was able to predict the Gram-status of CM causal pathogens using in-line sensor measurements from automatic milking systems. Data were collected at nine Dutch dairy farms milking with automatic milking systems and included 140 bacteriological cultured CM cases with sensor measurements of electrical conductivity, colors red, green, and blue and milk yield for analyses. In total, 110 CM cases were classified as Gram-positive CM cases and 30 as Gram-negative. Stratified randomization was used to divide the data in a training set (n = 96) for model development, and a test set (n = 44) for validation. The decision tree used three variables to predict the Gram-status of the CM causal pathogen; two variables were based on electrical conductivity measurements, and one on measurements of the color blue. This decision tree had an accuracy of 90.6% and a kappa value of 0.76 based on data in the training set. When only those CM cases were considered with extreme high probability estimates for their Gram-status (either positive or negative), 74% of all records in the training set could be classified with a stratified accuracy of 97.1%. When validated, the decision tree performed poorly; accuracy dropped to 54.5% and the kappa value to À0.20. The stratified accuracy calculated for 75% of all records in the test set was 66.7%. Predicting the CM causal pathogen showed a similar poor result; the decision tree had an accuracy of 27.9% and a kappa of 0.12, based on data in the test set. Based on these results, it is concluded that decision-tree induction in conjunction with sensor information from the electrical conductivity, color, and milk yield provide insufficient discriminative power to predict the Gram-status or the CM causal pathogen itself.

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Section: Discussionsupporting

confidence: 79%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Kamphuis

Mollenhorst

Hogeveen

2011

Computers and Electronics in Agriculture

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“…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%

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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“…In this respect, the evaluation of electrical conductivity (EC) represents an interesting approach, because inflammation modifies the concentration of anions and cations and can affect milk EC (Zaninelli and Tangorra, 2007). As a consequence, in dairy cows, this parameter has become one of the most studied indicators of udder HS and its use to detect mastitis has been largely adopted with successful results in terms of sensitivity and specificity (De Mol et al, 1999;Kamphuis et al, 2008, Maatje et al, 1992Nielen et al, 1995;Tangorra et al, 2006). On the contrary, in dairy goats, this has not been the case, mainly because only few, and sometimes contradictory studies were published on the relationship between EC and udder HS (Argüello, 2011).…”

Section: Introductionmentioning

confidence: 99%

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology -which provided interesting results in cows -was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

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Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk

Cited by 45 publications

References 12 publications

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

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

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

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