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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 R 2 of 0.8749. These values validate the proposed fuzzy logic system for helping to make decisions for better animal welfare.

ABSTRACT Considering the challenges faced by poultry farming, this study aimed to develop a neuro-fuzzy model to predict eyeball and crest temperatures of egg-laying hens based on environmental conditions (dry bulb temperature and relative humidity). To develop the models and simulations, Matlab’s Fuzzy Toolbox® (Anfisedit) was used. Different configurations were used for each of the several neuro-fuzzy models developed. Eyeball temperature (ET) and chicken crest temperature (CCT) were simulated from the developed neuro-fuzzy models, and the obtained results were validated with the variables collected experimentally with the aid of recorder sensors and an infrared thermographic camera. The proposed neuro-fuzzy models allow the accurate estimation of ET and CCT of two lineages of egg-laying hens raised in conventional aviaries, thus helping in decision-making for better animal welfare.

ABSTRACT Rapid cooling is necessary to remove heat from fruit; however, it directly affects the water loss of grapes under certain conditions. Fuzzy logic is an important theory for analyzing results as it simplifies the assimilation of the phenomena through refined adjustments, allowing the prediction of the weight loss of fruits in relation to rapid cooling conditions. The objective of this study was to develop a fuzzy model that could predict weight loss as a function of the rapid cooling of table grapes in different plastic film bags. Modeling was performed using three types of plastic film bags (micro-perforated, macro-perforated, and non-perforated) at three levels of palletization (lower, intermediate, and upper), arranged in an experimental design in randomized blocks, in a 3 × 3 factorial scheme, with three blocks. The influence of the relative humidity and amplitude of humidity on the variable weight loss percentage of the Arra 15 grape variety was measured. The average percentage error of the fuzzy model was 9.78%. The intermediate level alone showed an error of 4.02%. Thus, the developed fuzzy model provided a good prediction of the weight loss of table grapes.