Use of artificial neural networks to estimate production variables of broilers breeders in the production phase. Salle C.T.P., Guahyba A.S., Wald V.B., Silva A.B., Salle F.O. Probabilistic Neural Network Prediction of Ascites in Broilers Based on Minimally Invasive Physiological Factors. Roush W.B., Cravener T.L., Kochera Kirby Y. Association between persistency of lay and partial record egg production in white leghorn hens and implications to selection programs for annual egg production. Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Application of neural networks to modeling nonlinear relationships in ecology. Lek S., Delacoste M., Baran P., DimopoulosI., Lauga J. Characterization of poultry egg production using a multiphasic approach. Evaluation and Modeling of Physical and Physiological Damage to Wheat Seeds under Successive Impact Loadings: Mathematical and Neural Networks Modeling. Khazaei J., Shahbazi F., Massah J., Nikravesh M. Modeling physical damage and percentage of threshed pods of chickpea in a finger type thresher using artificial neural networks. A model for persistency of egg production. Neural networks for animal science applications: two case studies. Elsevier Science Publishers B.V., The Netherlands.įernandez C., Soria E., Martin J.D. Prediction of second parity milk performance of dairy cows from first parity information using artificial neural network and multiple linear regression methods. Improving Neural Network Prediction of Amino Acid Levels in Feed Ingredients. Genetic gains in annual egg production from selection on part-record percent production in the fowl. The results suggested that the ANN model could provide an effective means of recognizing the patterns in data and accurately predicting the egg production of laying hens based on investigating their age.īohren B.B., Kinney T.B. By increasing the summary intervals to 2 wk, 4 wk and then to 6 wk, ANN power was increased for prediction of egg production. We also estimated ANN parameters of a number of eggs on four data sets of individual hens. High R 2 and T for ANN model revealed that ANN is an efficient method of predicting egg production for pullet and hen flocks. An ANN model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variables. The focus of this study is on neural network applications to data analysis in egg production. Institute of health visitors (2015) neural networks (ANN) have shown to be a powerful tool for system modeling in a wide range of applications. Complementary feeding and baby led weaning. ![]()
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