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Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows.

G A Miller¹, M Mitchell², Z E Barker³

¹Department of Agriculture, Horticulture and Engineering Sciences, Scotland's Rural College, Peter Wilson Building, West Mains Road, King's Buildings, EdinburghEH9 3JG, UK.

Animal : an International Journal of Animal Bioscience

|January 14, 2020

Summary

Monitoring cow behavior with tail sensors can predict calving. This technology offers a reliable, non-invasive method for farmers to anticipate when a calf will be born, improving herd management.

Keywords:

animal-mounted sensors bovine parturition precision livestock farming random forest

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Transabdominal Ultrasound for Pregnancy Diagnosis in Reeves' Muntjac Deer

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Published on: January 7, 2014

Area of Science:

Animal Science
Agricultural Technology
Machine Learning

Background:

Increasing herd sizes reduce individual monitoring time for beef and dairy cows.
Pre-parturition behavioral changes in cows (e.g., reduced rumination, increased activity) can signal impending calving.
Non-invasive sensors offer a potential solution for monitoring these behavioral shifts.

Purpose of the Study:

To evaluate the efficacy of two sensor technologies (collars and tail-mounted accelerometers) for predicting calving in beef and dairy cows.
To develop and test machine learning models for calving prediction using single and combined sensor data streams.
To determine the optimal prediction window and sensor type for accurate calving prediction.

Main Methods:

Two trials involving 144 beef cows and 110 dairy cows were conducted.
Afimilk Silent Herdsman (SHM) collars monitored rumination (RUM), eating (EAT), and activity (ACT).
Axivity accelerometers on tails detected tail-raise events (TAIL).
Machine learning random forest algorithms were used to predict calving time.
Model performance was assessed using Matthew's correlation coefficient (MCC), AUC, sensitivity, and specificity.

Main Results:

Tail-mounted sensors (TAIL) showed slight superiority in predicting calving within a 5-hour window for beef (MCC=0.31) and dairy cows (MCC=0.29).
Integrating rumination and eating data (TAIL + RUM + EAT) yielded similar performance (MCC=0.32) for both breeds.
Combining SHM and tail sensor data did not significantly improve prediction accuracy over tail sensors alone.
Optimal calving prediction occurred 2 hours prior to expulsion for both beef (MCC=0.29) and dairy cows (MCC=0.25) using tail sensor data.

Conclusions:

Tail-mounted sensors alone are sufficient for predicting parturition in cows.
The optimal prediction window for calving using tail sensor data is 2 hours prior to the event.
This technology provides a practical, non-invasive tool for improving calving management in large herds.