Deep learning improves mastitis detection in automated milking systems

摘要

Automated milking systems (AMS) are already used in 10% of Canadian dairy farms and >20% of dairy farms in Western Canada, with indications of increasing adoption. Therefore, there is increasing need for accurate, early automated disease detection. Although many AMS models incorporate chemical sensors to assess milk, including detection of mastitis, udder health often declines in the year after transitioning. AMS generate much more data than milk characteristics. For example, some animal behavior data may be novel indicators for disease onset, with great potential to improve mastitis detection when analyzed with state-of-the-art machine learning methods.