The field of animal welfare takes care of the health and well-​being of animals so as to maintain a balance in the ecosystem. The key application of machine learning is in monitoring animal behaviour during the early exposure of infection.

  • Dutta et al. (2015) followed a two-​stage machine learning framework which is an effective method for classification of cattle behaviour. Cattle sensor technology and assemble classifiers are used in the current approach to categorize and examine the behavioural changes in cattle for improving their feed.
  • Pegorini et al. (2015) proposed a technique based on data collected by optical fibre Bragg grating sensors that are projected by machine learning technique (pattern classification). In this study, they have considered chewing process and food intake of dietary supplement. Furthermore, two more factors of hay and ryegrass that are ruminative and idleness for ingestion behaviour were considered. They showed that pattern classification differentiates the five patterns involved in chewing process.

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