Three neural network architectures—back-propagation-3, Ward-5, and the general regression neural network—were compared for their efficiency to forecast egg production, along with other traditional models.

Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initia…

Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes.

Feed costs significantly impact the profitability of egg production, representing more than 70% of the variable costs. This study evaluated the effect of dietary intakes of three essential amino acids (EAAs) on the egg cost for H&N Brown second-cycle laying hens.

Machine learning models for analysis and prediction of optimal egg ...

Integrating machine learning (ML) in poultry science presents transformative opportunities for optimizing production, enhancing animal welfare, and im…

In the rapidly evolving poultry industry, effective management is dependent on precise forecasting of production metrics. As producers seek to maximize yield while minimizing costs, the ability to accurately predict egg production parameters becomes essential.

This study suggests a mixed approach to predict the optimal age of hens for peak egg production by using mixed linear regression (MLR) models and through machine learning techniques. The primary objective is to enhance the accuracy and ...

Accurate egg production forecasting is essential for resource optimization, farm profitability, and sustainable poultry management. StackEgg, a stacked ensemble approach, is introduced in this study that combines a set of tree-based regressors ...

Three neural network architectures—back-propagation-3, Ward5, and the general regression neural network—were compared for their efficiency to forecast egg production, along with other traditional models.