Sensors and Clinical Mastitis-The Quest for the Perfect Alert

H. Hogeveen, Claudia Kamphuis, W. Steeneveld, H. Mollenhorst

Research output: Contribution to journalArticleAcademicpeer-review

131 Citations (Scopus)


When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models
Original languageEnglish
Pages (from-to)7991-8009
Issue number9
Publication statusPublished - 2010


  • automatic milking systems
  • dynamic light-scattering
  • somatic-cell count
  • dairy-cows
  • electrical-conductivity
  • bovine mastitis
  • neural-networks
  • detection model
  • abnormal milk
  • fuzzy-logic


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