Monitoring individual XXXX udder health in automated milking systems using online somatic cell counts.

@entity291 @entity1271
UNASSIGNED: This study presents and validates a detection and monitoring model for @entity1271 based on automated frequent sampling of online cell count (OCC). Initially, data were filtered and adjusted for sensor drift and skewed distribution using ln-transformation. Acceptable data were passed on to a time-series model using double exponential smoothing to estimate level and trends at @entity291 level. The OCC levels and trends were converted to a continuous (0-1) scale, termed elevated @entity1271 risk (EMR), where values close to zero indicate healthy @entity291 status and values close to 1 indicate high risk of @entity1271 . Finally, a feedback loop was included to dynamically request a time to next sample, based on latest EMR values or errors in the raw data stream. The estimated EMR values were used to issue 2 types of alerts, new and (on-going) intramammary infection (IMI) alerts. The new alerts were issued when the EMR values exceeded a threshold, and the IMI alerts were issued for subsequent alerts. New alerts were only issued after the EMR had been below the threshold for at least 8 d. The detection model was evaluated using time-window analysis and commercial herd data (6 herds, 595,927 milkings) at different sampling intensities. Recorded treatments of @entity1271 were used as gold standard. Significantly higher EMR values were detected in treated than in contemporary untreated @entity291 . The proportion of detected @entity1271 cases using new alerts was between 28.0 and 43.1% and highest for a fixed sampling scheme aiming at 24 h between measurements. This was higher for IMI alerts, between 54.6 and 89.0%, and highest when all available measurements were used. The lowest false alert rate of 6.5 per 1,000 milkings was observed when all measurements were used. The results showed that a dynamic sampling scheme with a default value of 24 h between measurements gave only a small reduction in proportion of detected @entity1271 treatments and remained at 88.5%. It was concluded that filtering of raw data combined with a time-series model was effective in detecting and monitoring @entity1271 status in dairy @entity291 when based on IMI alerts, and by using a dynamically adjusting sampling scheme almost full performance was still obtainable. However, results were less desirable when based on new alerts most likely because of the used gold standard for @entity1271 , which may not necessarily reflect the onset of and IMI case in contrast to a new alert.
