Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter

Mol, R.M. de; Keen, A. R.; Kroeze, G.H.; Achten, Juul

doi:10.1016/s0168-1699(99)00016-2

Cited by 66 publications

(48 citation statements)

References 9 publications

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“…Furthermore, Hyun et al (1997) observed that increased visits to the water source was correlated with increased time engaged in feeding-related activities, which resulted in improved ADFI and ADG in growing-finishing pigs. The existence of a drinking pattern and the specificity of drinking behavior are criteria that allow using drinking behavior as a predictor for health or production problems (de Mol et al, 1999). Friend (1973) showed that water intake varied with estrus, and Madsen and Kristensen (2005) showed that changes in drinking pattern preceded an outbreak of diarrhea in pigs 29 d after weaning.…”

Section: Discussionmentioning

confidence: 99%

Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers12

Meiszberg

Johnson

Sadler

et al. 2009

Journal of Animal Science

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Assimilating accurate behavioral events over a long period can be labor-intensive and relatively expensive. If an automatic device could accurately record the duration and frequency for a given behavioral event, it would be a valuable alternative to the traditional use of human observers for behavioral studies. Therefore, the objective of this study was to determine the accuracy in the time spent at the waterer and the number of visits to the waterer by individually housed nursery pigs between human observers scoring video files using Observer software (OBS) and an automatic water meter Hobo (WM, control) affixed onto the waterline. Eleven PIC USA genotype gilts (22 ± 2 d of age; 6.5 ± 1.4 kg of BW) were housed individually in pens with ad libitum access to a corn-based starter ration and one nipple waterer. Behavior was collected on d 0 (day of weaning), 7, and 14 of the trial using 1 color camera positioned over 4 attached pens and a RECO-204 DVR at 1 frame per second. For the OBS method, 2 experienced observers recorded drinking behavior from the video files, which was defined as when the gilt placed her mouth over the nipple waterer. Data were analyzed using nonparametric methods and the general linear model and regression procedures in SAS. The experimental unit was the individual pen housing 1 gilt. The GLM model included the method of observation (WM vs. OBS) and time (24 h) as variables, and the gilt nested within method was used as the error term. Gilts consumed more water (P = 0.04) on d 14 than on d 0. The time of day affected (P < 0.001) the number of visits and the time spent at the waterer regardless of the method. However, the OBS method underestimated (P < 0.001) the number of visits to the waterer (3.48 ± 0.33 visits/h for OBS vs. 4.94 ± 0.33 for WM) and overestimated (P < 0.001) the time spent at the waterer (22.6 ± 1.46 s/h for OBS vs. 13.9 ± 1.43 for WM) compared with WM. The relationship between the 2 methods for prediction of time spent at the waterer and number of visits made by the gilts was weak (R 2 = 0.56 and 0.69, respectively). Collectively, these data indicate that the use of the traditional OBS method for quantifying drinking behavior in pigs can be misleading. Quantifying drinking behavior and perhaps other behavioral events via the OBS method must be more accurately validated. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. ABSTRACT: Assimilating accurate behavioral events over a long period can be labor-intensive and relatively expensive. If an automatic device could accurately record the duration and frequency for a given behavioral event, it would be a valuable alternative to the traditional use of human observers for behavioral studies. Therefore, the objective of this study was to determine the accuracy in the time spent at the waterer and the number of visits to the waterer by individually housed nursery pigs between human observers scoring...

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Section: Discussionmentioning

confidence: 99%

Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers12

Meiszberg

Johnson

Sadler

et al. 2009

Journal of Animal Science

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“…The statistical model calculates estrus alerts, which were input for the fuzzy logic model, in which they are classified true or false. The statistical model generated an alert when the combination of sensor measurements fell outside a confidence interval: 95, 99, or 99.9% (De Mol et al, 1999).…”

Section: Fuzzy Logic Model For the Classification Of Estrus Alertsmentioning

confidence: 99%

“…The sensor measurements are input data for a detection model, with alerts for estrus, mastitis, and other diseases as output data. A detection model for estrus and mastitis has been developed in previous research (De Mol et al, 1999). The results from this statistical model can be satisfactory if the sensor equipment performs well (De Mol et al, 1997.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Logic in Automated Cow Status Monitoring

Mol¹,

Woldt

2001

Journal of Dairy Science

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Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.

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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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Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter

Cited by 66 publications

References 9 publications

Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers12

Drinking behavior in nursery pigs: Determining the accuracy between an automatic water meter versus human observers12

Application of Fuzzy Logic in Automated Cow Status Monitoring

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

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