Machine Learning Prediction of Thermal Stress in Indigenous Sheep

Abstract

Introduction: Thermal stress is one of the key problems that exist in sheep farming as it has a negative impact on the physiology, wellbeing, performance, and adaptation of the animals in the changed environment. The breeds of indigenous sheep have their adaptability characteristics; however, any drastic changes in temperature, humidity and other environmental stresses will have an adverse impact on the physiology of the sheep. The current study introduces the new method of predicting thermal stress in indigenous sheep breeds taking into account the environmental and physiological parameters. This novel approach included analysis of the parameters such as temperature, humidity, temperature humidity index (THI), body temperature, respiration rate, and reactions of sheep in order to determine the state of stress. This information was collected and analyzed with the help of machine learning algorithms in order to predict the level of thermal stress and the factors which have influence on the process