Biography

Assistant Professor in Veterinary Medicine with a Data Science twist.

Talk

Predicting the Moment of Birth using Sensor Data in Dairy Cows

Stillbirth, defined as calves that die during unobserved birth is often seen as an indicator of lowered animal welfare in dairy cows. Sensors have been proposed as a tool to support dairy farmers but accurate calving prediction models are often lacking. In this session, a machine learning data pipeline will be described using the spark ML framework. Heavy lifting and feature preparation for sensor data from 1331 cows on 8 herds from 21 days before until the day of calving was performed using sliding windows and time series analysis. In total a set of 100+ features was used in a random forest classification model trained and tested using different cross validation approaches. These different approaches were applied to avoid overfitting to specific cow and herd effects, assuring robust industry applicable models. The data pipeline created a ML model to predict the exact day of calving in dairy cows with an accuracy of 95%, offering a method to diminish unobserved calvings and stillbirth in dairy cows.

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