An investigation into the use of machine learning for determining oestrus in cows

Abstract A preliminary investigation of the application of two well-known machine learning (ML) schemes — C4.5 and FOIL — to detection of oestrus in dairy cows has been made. This is a problem of practical economic significance as each missed opportunity for artificial insemination results in 21 days lost milk production. Classifications were made on normalised deviations of milk volume production and milking order time series data. The best learning scheme was C4.5, which detected 69% of oestrus events, albeit with an unacceptably high rate of “false positives” (74%). Further work based on the use of a progesterone assay to provide a more accurate oestrus reference is suggested, along with the inclusion of more monitored variables and an analysis of their relative contributions to the learning process.