INFRARED THERMOGRAPHY AND MACHINE LEARNING IN LIVESTOCK PRODUCTION

Abstract

This review presents infrared thermography, its application in livestock production and its
integration with machine learning algorithm to provide an end-to-end solution towards
enhancedproductivity. Infrared thermography is a simple non-contact, non-invasive method to
detect surface temperature radiated from an animal skin. Temperature data is used to generate
images called thermograms which can be used for diagnosis of diseased and non-diseased
conditions. Real time collection of thermal data has resulted in huge volume of data, which
requires the use of machine learning algorithms to assist the farmers gain insights that could be
used to make informed decisions. The potential of the integration of machine learning and
infrared thermography in livestock production have been explored. The areas of application
include identification of unique features of individual animals, real time tracking of animals and
determining breathing patterns that could indicate stress and pain. With improved understanding
of how machine learning algorithms can be integrated with infrared thermography, farmers can
explore other areas of application of both infrared thermography and machine learning to
improve health, welfare and productivity of livestock.