Non-invasive measure of heat stress in sheep using machine learning techniques and infrared thermography

Abstract

Heat stress (HS) leads to altered sheep behavior, physiological, and biochemical processes which negatively affects their welfare and performance. While suitable strategies are needed to ameliorate the impacts of HS in sheep, it is equally important to accurately and non-invasively measure HS. Traditionally, rectal temperature (RT) is considered an indicator of thermal balance and is used to assess the impacts of hot conditions on sheep. However, measuring RT itself can be a stressor as it often requires restraining of the animals. The main objective of this study was to establish whether a combination of infrared thermography (IRT) and machine learning techniques can be applied to predict s..