A fuzzy system for cloacal temperature prediction of broiler chickens

Júnior

et al. 2020

BJD

Section: Introductionunclassified

Avaliação Dos Parâmetros Ambientais E Fisiológicos Para Frangos De Corte Linhagem Caipira Em Diferentes Fases De Criação Na Amazônia Ocidental

Júnior

et al. 2020

BJD

“…Thus, when environmental conditions are not within the range considered as suitable, which is characterized by the thermoneutral zone, the environment becomes uncomfortable. An important parameter to determine the comfort condition of birds is the cloacal temperature (Ferreira et al, 2012). The variation of cloacal temperature is a physiological response to the air temperature and relative humidity conditions resulting from the storage or dissipation of metabolic heat (Silva et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Model for Predicting Cloacal Temperature of Broiler Chickens Under Thermal Stress

et al. 2019

Self Cite

Broiler chickens submitted to different intensities and durations of thermal stress are subject to variation in cloacal temperature and, consequently, to a decrease in performance. Given the complexity of these interactions, artificial intelligence is a useful methodology for decision-making. Thus, this study aimed to assess and predict, by means of a fuzzy model, the cloacal temperature of broiler chickens submitted to thermal stress in the second week of life, with varying durations and intensities, in climatized wind tunnels. Mamdani's inference and defuzzification methods by means of the center of gravity were used. One hundred and twenty rules were elaborated. The developed fuzzy model was used to predict the cloacal temperature of broiler chickens on the day of thermal stress as a function of air dry-bulb temperature (°C), stress duration (days), and the number of days after thermal stress. Cloacal temperature values simulated by the fuzzy model showed standard deviations and percentage errors lower than 0.02 and 0.08%, respectively, when compared to those obtained experimentally.

“…The microclimatic conditions in the breeding environment have a strong effect on the thermal comfort of broiler chickens (Mirzaee-Ghaleh et al, 2015). Among the environmental factors, air temperature and relative humidity are the most relevant to poultry because they affect the control of body temperature and heat exchange for maintaining homeothermy (Costa et al, 2012;Ferreira et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Performance of Fuzzy Inference Systems to Predict the Surface Temperature of Broiler Chickens

et al. 2018

Self Cite

This study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 ºC) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected.