Latent variable models in the understanding of animal monitoring data

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper looks at techniques in the field of machine learning that can be employed to aid the interpretation of intensively gathered sen-sor data from domestic livestock. Given the high levels of reliability afforded through improved battery technology and progressively more powerful small computing devices, condition monitoring on such scales has become widespread but at the expense of the understanding of the relation to the welfare condition that underlies the quantities being measured. Latent class models offer a means of postulating the existence of an abstraction or category label for a given set of observations. In this chapter we look at the additional understanding that 3 progressively sophisticated models can offer in the interpretation of a set of GIS data gathered from a herd of 15 beef cows. We conclude with a review of practical applications where these models may assist understanding of the potentially complex behavioural relationships between individuals and groups of animals.
Original languageEnglish
Title of host publicationSmart Sensing Technology for Agriculture and Environmental Monitoring
EditorsSubhas Mukhopadhyay
Place of PublicationBerlin
PublisherSpringer
Pages119-134
Number of pages16
Volume146
ISBN (Print)9783642276378
DOIs
Publication statusPublished - 2012

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer
Volume146
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Keywords

  • machine learning
  • geospatial information systems
  • animal behaviour
  • condition monitoring

Cite this

Stephen, B., Michie, W., & Andonovic, I. (2012). Latent variable models in the understanding of animal monitoring data. In S. Mukhopadhyay (Ed.), Smart Sensing Technology for Agriculture and Environmental Monitoring (Vol. 146, pp. 119-134). (Lecture Notes in Electrical Engineering; Vol. 146). Springer. https://doi.org/10.1007/978-3-642-27638-5