Estimate of the weight of japanese quail eggs through fuzzy sets theory

Abstract

<jats:p>Quail breeding is a viable alternative for animal production and due to its low investment, fast return of invested capital, use of small areas and creation of jobs has aroused much interest in Brazil. The aim of this study was to develop a model based on fuzzy set theory to predict the weight of eggs from Japanese quails. The proposed fuzzy model was based on data from field measurement experiments, as well as from literature referring to the influence of environment over the weight of eggs. To develop the fuzzy system, air dry-bulb temperature (t db, ° C) and relative air humidity (RH, %) were defined as input variables and trapezoidal and triangular membership functions were used, respectively. The absolute deviation between the values for observed egg weight and egg weight estimated by the fuzzy system, varied between 0.01 g and 0.32 g, and the average deviation was 0.14 g. The average error found was 2.33%, and the determination coefficient (R²) was equal to 0.668. The fuzzy system developed to estimate the weight of Japanese quail eggs, based on the t db and RH provided low values for absolute deviation and percentage error, allows a realistic estimate of the weight of eggs in different environmental conditions.</jats:p>

Fuzzy index for swine thermal comfort at nursery stage based on behavior

“…The coefficient of determination found in the present study was higher than that found by [35], lower than those obtained by [14], and close to those observed by [16].…”

“…Estimating the weight of Japanese quail eggs, [35] obtained a coefficient of determination of 66.80%. According to these authors, the developed fuzzy model provides a realistic estimate of egg weight.…”

Índice Fuzzy Para O Conforto Térmico De Suínos Na Fase De Crescimento E Terminação Com Base Na Temperatura Superficial E Frequencia Respiratória

“…O desvio padrão médio encontrado no presente trabalho foi menor que os encontrados por Schiassi et al (2015), Ponciano et al(2012) e Campos et al (2013) Estudando a estimativa do peso de ovos de codornas japonesas com uso de modelo fuzzy, Castro et al (2012) obtiveram um coeficiente de determinação de 66,80%. Entretanto, Campos et al (2013), ao estudarem a taxa de ocupação em instalações para gado de leite, obtiveram um coeficiente de determinação de 75,45%.…”

“…O coeficiente de determinação do presente trabalho foi menor que os encontrados por Schiassi et al (2015), Ponciano et al (2012), Campos et al (2013) e próximos ao obtido por Castro et al (2012). Com base na literatura citada, observa-se que, de acordo com os coeficientes de determinação encontrados (58,72% e 83,55%), o modelo mostrou-se adequado e satisfatório apenas para a predição da frequência respiratória, auxiliando no controle do ambiente térmico para suínos, enquanto para respostas de temperatura superficial mostrou-se não satisfatório, uma vez que esse coeficiente de determinação é considerado baixo quando trabalha-se com animais.…”

Pertinence Curves in Fuzzy Modeling of the Productive Responses of Broilers

“…Therefore, it is essential to develop algorithms for the control of the environment inside the aviaries. Among them, models based on artificial intelligence are gaining research interest because the fuzzy methodology has proved to be effective in research with animal comfort (Castro et al, 2012;Ponciano et al, 2012;Campos et al, 2013;Aborisade & Stephen, 2014;Ferraz et al, 2014;Xiang-Jie, 2014;Julio et al, 2015;Mirzaee-Ghaleh et al, 2015;Schiassi et al, 2015). However, so far fuzzy systems in the area of animal ambience have been developed only with the use of one or two types of pertinence curves, the commonly used being triangular or trapezoidal.…”