Comparison of logistic and neural network models to fit to the egg production curve of White Leghorn hens

Savegnago, Rodrigo Pelicioni; Nunes, Berta; Caetano, S. L.; Ferraudo, Antônio Sérgio; Schmidt, G. S.; Ledur, Mônica Corrêa; Munari, Danísio Prado

doi:10.3382/ps.2010-00723

Cited by 31 publications

(32 citation statements)

References 23 publications

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“…They suggested that the Narushin-Takma model was a good alternative for modelling yield of eggs. Savegnago et al (2011) analysed annual egg production of White Leghorn strains with logistic and neural network models. It was concluded that the neural networks could be used as an alternative tool to fit egg production.…”

Section: Discussionmentioning

confidence: 99%

“…It was concluded that the neural networks could be used as an alternative tool to fit egg production. In another study, conducted by Savegnago et al (2011), weekly egg production rates of selected (for egg productionand egg quality) and non-selected (random bred control) lines of a White Leghorn henpopulation were used to fit non-linear (logistic, compartmental, modified compartmental, McMillan, McNally) and multiphasic (segmented polynomial, persistency) models. According to the goodness of fit of the models, the logistic, modified compartmental, segmented polynomial, and persistency models presented the best goodness of fit.…”

Section: Discussionmentioning

confidence: 99%

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Effect of age on production characteristics of Boschveld indigenous chickens of South Africa reared intensively

Okoro¹,

Ravhuhali²,

Mapholi³

et al. 2017

SA J. An. Sci.

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The Boschveld indigenous chicken is a breed that was developed in South Africa for rural household free-range production of meat and eggs. A performance test was carried out to identify their production characteristics when reared intensively, and to estimate the responses of the production characteristics to age, based on optimization functions. Day-old chicks were procured from the breeder and reared to point of lay, with 1432 layers generating the data. Parameters include egg weight (g), egg number, hen day egg production (HDEP) (%), feed intake (g/b/d), bodyweight (kg), bodyweight gain (g), feed conversion ratio (FCR), feed efficiency (FE), and mortality from start of lay until 44 weeks old. There was a significant effect of age on all these production parameters, except for FCR, which was not affected significantly by age as the birds grew. On fitting a prediction and optimization function using a regression model, these parameters showed significant linear response to age: HDEP, feed intake (g), and bodyweight (kg), while significant quadratic responses were found in egg weight and FCR. The prediction model shows that at age 34, 52 and 65 weeks of lay, bodyweight, HDEP and feed intake respectively, would be optimized, while at age 36 and 43 weeks, FCR and egg weight respectively, would be optimized. This implies that Boschveld indigenous chicken production characteristics can be targeted at the appropriate age and can be optimized, given the age of lay that the birds are in.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effect of age on production characteristics of Boschveld indigenous chickens of South Africa reared intensively

Okoro¹,

Ravhuhali²,

Mapholi³

et al. 2017

SA J. An. Sci.

View full text Add to dashboard Cite

show abstract

“…In the present study, the tested architectures that showed the best BM prediction performance were the multilayer networks (multilayer perceptron, mLP), which have been widely used for the development of an ANN (Savegnago et al, 2010;Kaewtapee et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Predicting chick body mass by artificial intelligence-based models

Ferraz

Yanagi

Julio

et al. 2014

Pesq. agropec. bras.

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-The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) -with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks -was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.Index terms: animal welfare, artificial neural network, broiler, modeling, neuro-fuzzy network, thermal comfort. Predição da massa corporal de pintinhos por meio de modelos baseados em inteligência artificialResumo -O objetivo deste trabalho foi desenvolver, validar e comparar 190 modelos baseados em inteligência artificial, para predizer a massa corporal de pintinhos de 2 a 21 dias de vida, submetidos a diferentes períodos e intensidades de estresse térmico. O experimento foi realizado com 210 pintinhos, em quatro túneis de vento climatizados. Um banco de dados com 840 conjuntos de dados (de aves de 2 a 21 dias) -com as variáveis temperatura de bulbo seco do ar, duração do estresse térmico (dias), idade das aves (dias) e a massa corporal diária dos pintinhos -foi utilizado para treinamento de rede, validação e testes dos modelos baseados em redes neurais artificiais (RNA) e redes "neuro-fuzzy" (RNF). As RNA mostraram-se mais precisas para se predizer a massa corporal de pintinhos de 2 a 21 dias de idade, submetidos às variáveis de entrada, e apresentaram R² de 0,9993 e erro-padrão de 4,62 g. As RNA propiciam a simulação de diversos cenários, que podem auxiliar na tomada de decisões em relação ao manejo, e podem ser incorporadas nos sistemas de controle de aquecimento.Termos para indexação: bem estar animal, redes neurais artificiais, frango, modelagem, redes neurais difusas, conforto térmico.

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“…(18,19). Pero lo más relevante de las RNA radica en su capacidad de aprender y reestructurarse a sí misma, convirtiéndola en un modelo que está en constante adaptación (20).…”

Section: Introductionunclassified

Modelación del crecimiento de pollitas Lohmann LSL con redes neuronales y modelos de regresión no lineal

Galeano-Vasco

Cerón-Muñoz

2013

Rev MVZ Córdoba

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RESUMEN Objetivo. Modelar la curva del crecimiento de aves de la línea Lohmann LSL utilizando modelos no lineales (MNL), no lineales mixtos (MNLM) y redes neuronales artificiales (RNA). Materiales y métodos.Periódicamente se pesaron 33 aves en promedio, desde el día 21 al 196 de vida para un total de 558 registros individuales de peso. En el ajuste de la curva de crecimiento se utilizaron los modelos: no lineal de Von Bertalanffy (MNL), no lineal Mixto de Von Bertalanffy (MNLM) y redes neuronales artificiales (RNA). Los modelos se compararon con coeficiente de correlación y medidas de presicion cuadrado medio del error (CME), desviación media absoluta (MAD) y porcentaje de la media absoluta del error (MAPE). Resultados. Los valores de correlación entre los datos reales y estimados, fueron 0.999, 0.990 y 0.986 para MNLM, RNA y MNL respectivamente. El modelo más preciso con base en los criterios MAPE, MAD y CME fue el MNLM, seguido por la RNA. La grafica de predicción generada la RNA es similar a la del MNLM. La RNA presentó un desempeño superior al MLN. Conclusiones. El mejor modelo para la predicción de curvas de crecimiento de aves comerciales de la línea Lohmman LSL hasta los 196 días de edad, con múltiples mediciones por animal en el tiempo, fue el MNLM. La RNA presento un desempeño superior al MNL.Palabras clave: Crecimiento, modelo no lineal, modelo no lineal mixto, redes neuronales artificiales (Fuente: MeSH). ABSTRACTObjective. Modeling the pullet growth curve of the Lohmann LSL line, by using nonlinear model (MNL), nonlinear mixed model (MNLM) and artificial neural networks (ANN). Materials and methods. An average of 33 birds, were weighed from day 21 to 196 of life for 558 individual weight records. To adjust the growth curve the following models were used: nonlinear Von Bertalanffy (MNL), nonlinear mixed Von Bertalanffy (MNLM) and artificial neural networks (RNA). The models were compared with a correlation coefficient and precision measurements: mean square error (MSE), Mean Absolute Deviation (MAD) and the mean absolute percentage error (MAPE). Results. Correlation values, between actual and estimated data, were 0.999, 0.990 and 0.986 for MNLM, RNA and MNL respectively. The most accurate model based on the MAPE, MAD and CME criteria was MNLM followed by RNA. The prediction graph for RNA was similar to MNLM. The RNA performance was higher than MLN. Conclusions. The best model for the prediction of growth curves of commercial Lohmman LSL birds to 196 days of age, was the MNLM, with multiple measurements per animal at the time. RNA performance was higher MLN.

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Comparison of logistic and neural network models to fit to the egg production curve of White Leghorn hens

Cited by 31 publications

References 23 publications

Effect of age on production characteristics of Boschveld indigenous chickens of South Africa reared intensively

Effect of age on production characteristics of Boschveld indigenous chickens of South Africa reared intensively

Predicting chick body mass by artificial intelligence-based models

Modelación del crecimiento de pollitas Lohmann LSL con redes neuronales y modelos de regresión no lineal

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