FUZZY LOGIC MODELING OF THE OCULAR TEMPERATURE OF CATTLE IN THERMAL STRESS CONDITIONS

Abstract

This study proposes a fuzzy logic model capable of predicting the ocular temperature (OT) of beef cattle by means of infrared thermography. The goal of this study is to establish a methodology for making decisions related to animal welfare. The experiment was carried out at a commercial beef production farm, located in the south of Minas Gerais state, where twenty-eight Brahman cattle (Bos Taurus Indicus) raised in extensive production systems were evaluated. Thermal images of the entire head of the animal were collected in order to measure the ocular temperature (OT). Concurrently, the variables air dry bulb temperature (DBT) and relative humidity (RH) were recorded. The fuzzy logic model was developed using the Mandani inference method, based on the input variables DBT and RH and the output variable OT, and using the experimental data as reference. The proposed fuzzy logic system allows the estimation of the ocular temperature of beef cattle with an error of 1.71% and a coefficient of determination R2 of 0.8749. These values validate the proposed fuzzy logic system for helping to make decisions for better animal welfare.