prediction of egg production using artificial neural network

artificial neural networks (ann) have shown to be a powerful tool for system modeling in a wide range of applications. the focus of this study is on neural network applications to data analysis in egg production. 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. high r2 and t for ann model revealed that ann is an efficient method of predicting egg production for pullet and hen flocks. we also estimated ann parameters of a number of eggs on four data sets of individual hens. by increasing the summary intervals to 2 wk, 4 wk and then to 6 wk, ann power was increased for prediction of egg production. 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.