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Auflistung nach Autor:in "Cavero, D."

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  • Konferenzbeitrag
    Auswertung serieller Daten zur Mastitiserkennung mit Hilfe der lokalen Regression
    (Integration und Datensicherheit – Anforderungen, Konflikte und Perspektiven, Referate der 25. GIL Jahrestagung, 2004) Cavero, D.; Tölle, K.-H.; Krieter, J.
    The aim of this study was to detect incidence of mastitis in an automatic milking system using serial information. Data from 112,000 milkings of the research dairy herd Karkendamm of the University of Kiel were available. The incidence of mastitis was defined both on therapies carried out and on weekly somatic cell count measurements. The time series of electric conductivity of quarter milk were analysed to find deviations as a sign for mastitis. Three methods were performed to find mastitis cases. First, a local regression method using the SAS-procedure LOESS, second a moving average system and third an exponentially weighted moving average were applied. The used methods provided similar results. The goodness of alerts varied dependent on the threshold value. A low threshold (3\%) led to a sensitivity of nearly 100\%, however the specifity was only about 30% and thus the error rate was high (about 70%). With increasing thesholds (7%) sensitivity decreased to 70% and specifity increased to 75%. Error rate was slightly reduced to 60%.
  • Konferenzbeitrag
    Mastitis detection in dairy cows using Neural Networks
    (Agrarinformatik im Spannungsfeld zwischen Regionalisierung und globalen Wertschöpfungsketten – Referate der 27. GIL Jahrestagung, 2007) Krieter, J.; Cavero, D.; Henze, C.
    The aim of the present research was to investigate the usefulness of neural networks (NN) in the early detection and control of mastitis in cows milked in an automatic milking system. A data set of 403,537 milkings involving 478 cows was used. Mastitis was determined according to udder treatment and/or somatic cell counts (2). Mastitis alerts were generated by a NN model using electrical conductivity, milk production rate, milk flow rate and days in milk as input data. The evaluation of the model was carried out according to block-sensitivity, specificity and error rate. When the block-sensitivity was set to be at least 80%, the specificities were 51.1% and 74.9% and the error rates were 51.3% and 80.5% for mastitis definitions 1 and 2, respectively. Additionally, the average number of true positive cows per day ranged from 1.2 to 6.4, and the average number of false negative positive cows per day ranged from 5.2 to 6.8 in an average herd size of 24 cows per day for the test data.