Predictive models for heat stress assessment in Holstein dairy heifers using infrared thermography and machine learning.

Heat stress is a condition that impairs the animal's productive and reproductive performance, and can be monitored by physiological and environmental variables, including body surface temperature, through infrared thermography. The objective of this work is to develop computational models for classification of heat stress from respiratory rate variable in dairy cattle using infrared thermography. The database used for the construction of the models was obtained from 10 weaned heifers, housed in a climate chamber with temperature control, and submitted to thermal comfort and heat wave treatments.

Thermal signature: A method to extract characteristics from infrared thermography data applied to the development of animal heat stress classifier models.

Recent studies have investigated the use of infrared thermography (IRT) to monitor body surface temperature and correlate it with factors related to animal welfare and performance. In this context, this work proposes a new method for extracting characteristics for the temperature matrix obtained using IRT data from regions of the body surface of cows which, if associated with environmental variables through a machine learning algorithm it generates computational classifiers for heat stress condition. IRT data were collected from different parts of the body of 18 lactating cows housed in a free-stall, monitored for 40 non-consecutive days, three times a day (5:00 a.