Data Analysis in Robotic Milking Systems

Automatic milking – also referred to as robotic milking – is an important step towards precision dairy farming and is increasingly adopted by dairy farmers. The first commercially available automatic milking systems were introduced in the Netherlands in 1992. Ever since, the global milking robots market worth was estimated at USD 1.25 billion in 2019 and is expected to reach USD 2.94 billion by 2027 (Fortune Business Insights report, 2019).

Milking procedure consumes about 25% to 35% of the annual labor time in conventional milking systems. Reduction of time spent on repetitive manual tasks, difficulties regarding the availability and cost of competent labor force and the need for higher productive efficiency with lower inputs lead to an increasing demand for automatic milking. However, conversion from conventional to automatic milking, also referred to as robotic milking, comes with several challenges for herd health and farm management.

Robotic milking also comes with a plethora of valuable field data, usually updated on daily basis. Our vision is to utilize these large datasets from all over the world to create a diverse database. Implementing data analysis and training machine learning algorithms on this database can lead to the development optimal predictive models.

We have trained a machine learning model on robotic milking data from cattle dairy farms. This model performs predictions on mastitis and assesses herd udder health based on milk samples data.

We provide a service available to farmers and consultants worldwide, aiding:

  • ealry diagnosis and prevention of mastitis
  • data-driven decision making
  • reduction of antimibiotics’ use (One Health Initiative), and
  • protection of animal welfare