Egg hatchability prediction by multiple linear regression and artificial neural networks

Bolzan, AC; Machado, Raf; Piaia, Jcz

doi:10.1590/s1516-635x2008000200004

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…This is in consideration of its robustness in tackling noisy input data, high tolerance to faults and dimensionality problem and generalization from the input data. According to Bolzan et al (2008), ANN model outperformed its multiple linear counterpart in the prediction of hatched eggs. Mehri (2013) reported ANN-based model with a better accuracy (R 2 = 0.99) than that obtained in the present study.…”

Section: Discussionmentioning

confidence: 99%

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Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network

Yakubu

Dahloum

Shoyombo

et al. 2019

J. Indonesian Trop. Anim. Agric.

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This study was embarked upon to predict hatchability and mortality rate of Muscovy ducks in Nasarawa State, Nigeria. Data were obtained from a total of 119 duck farmers. The automatic linear modelling (ALM) and artificial neural network (ANN) models were employed. The average flock size was 9.84±0.60 per household. The predicted hatchability mean values using ALM (8.66) and ANN (8.65) were similar to the observed value (8.66). The predicted mortality mean values using ALM (2.95) and ANN (3.03) were also similar to the observed value of 2.95. Experience in duck rearing, the educational status of farmers, source of foundation stock and season were the variables of importance in the prediction of hatchability using ALM and ANN models. However, primary occupation, source of foundation stock, experience in duck rearing, land holding and management system were the important variables automatically selected for the prediction of mortality. Moderate coefficients of determination (R2 = 0.422 vs 0.376) and adjusted R2 (0.417 vs 0.371) estimates were obtained for hatchability and mortality using ALM. Different patterns were obtained under the ANN models as regards the prediction of hatchability (R2= 0.573 and adjusted R2= 0.569) and mortality (R2= 0.615 and adjusted R2= 0.612). The present information may aid management decisions towards better hatchability and mortality performance in Muscovy ducks.

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Section: Discussionmentioning

confidence: 99%

“…ANN is a non-linear parametric model that mimics the processing mechanism of the human brain. There is increasing use of this algorithm to predict hatchability (Bolzan et al, 2008), growth (Yakubu et al, 2018a) and egg production (Ahmad, 2011). It has also been used to model disease occurrence (Akil and Ahmad, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network

Yakubu

Dahloum

Shoyombo

et al. 2019

J. Indonesian Trop. Anim. Agric.

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“…The goodness of fit of the model and the accuracy of the predicted AMEn were evaluated using the training and testing data. The measures used in this process were as follows: Coefficient of determination (R 2 ), m.s.e., mean absolute deviation (MAD), mean absolute percentage error (MAPE) and bias, as defined in Bolzan et al (2008) and Perai et al (2010).…”

Section: Model Developmentmentioning

confidence: 99%

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs' energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R 2 , mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel R AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.Keywords: avian production, broilers, metabolisable energy, multilayer perceptron ImplicationsIt is difficult and expensive to estimate the nitrogencorrected metabolisable energy (AMEn) values of the feedstuffs used for broilers. The results of this study demonstrate that the implementation of multilayer perceptron networks (MLP) in a meta-analysis is suitable to estimate these energy values. Furthermore, a calculator was created on the basis of the results of the MLP, which allows an efficient way of predicting the AMEn values.

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“…Small deviations from the recommended values may increase embryonic mortality and consequently, reduce hatchability rates (Bolzan et al, 2008). According to Calil (2007), the physiological needs of the embryo change according to their developmental stage and, therefore, the maintenance of temperature during incubation is one of the most important factors influencing embryonic development.…”

Section: Introductionmentioning

confidence: 99%

Effect of Incubator Type and Broiler Breeder Age on Hatchability and Chick Quality

Araújo

Leandro

Mesquita

et al. 2016

Rev. Bras. Cienc. Avic.

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Two experiments were conducted to evaluate the effect of broiler breeder age and incubator type on hatching parameters, hatch window, embryo diagnosis results, and hatchling physical quality. The treatments consisted of a combination of three broiler breeder ages (29, 35 and 59 weeks of age) and two incubator types (single stage, SS; or and multiple stage, MS). A completely randomized design in a 3x2 factorial arrangement was applied. In Experiment I, 1,896 eggs were used and 360 eggs in Experiment II. There was an interaction between breeder age and incubator type only for hatchling physical quality score. Independently of incubator type, hatchability rate, late embryo mortality, and egg contamination were higher in the eggs laid by older breeders (59-wk-old). Early mortality (0-4 days) was higher in the embryos from young breeders (29-wk-old). A shorter hatch window birth was obtained in the SS incubator, resulting in higher hatchling body weight relative to egg weight, and better hatchling physical quality score. Both types of incubators provide good conditions for embryo development; however, the physical quality of chicks derived from eggs from intermediate-aged breeders (35-wk-old) is better when eggs are incubated in SS incubators.

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Egg hatchability prediction by multiple linear regression and artificial neural networks

Cited by 16 publications

References 12 publications

Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network

Modelling hatchability and mortality in muscovy ducks using automatic linear modelling and artificial neural network

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Effect of Incubator Type and Broiler Breeder Age on Hatchability and Chick Quality

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