ArticlePoultry science2020
Prediction and optimization of slaughter weight in meat-type quails using artificial neural network modeling.
Article in Poultry science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.Tropical animal health and production · 2026Article
- Application of Artificial Neural Network and the Monomolecular Model in Describing the Relationship Between Body Weight Gain and Metabolizable Energy Intake in Egg-Type Pullets.Veterinary medicine and science · 2026Article
- Optimizing Feeding Schedule and Live-Weight Prediction for Native Chicken Based on Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Trade-off between feed efficiency and immunity in specialized high-performing chickens.Poultry science · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carcass yield of meat-type quails is strongly correlated with the weight of the birds at slaughter (slaughter weight [SW]; body weight at 45 D of age). Moreover, prediction of superior animals for SW at the earlier stages of the rearing period is favorable for producers. Therefore, the aim of the present study was to predict and optimize SW of Japanese quails based on their early growth performances, sex, and egg weight as predictors through artificial neural network (ANN) modeling. To construct the ANN model a feed-forward multilayer perceptron neural network structure was used. Moreover, sensitivity analysis was used to arrange the predictors in the ANN model(s) according to their predictive importance too. In addition, the optimization process was conducted to determine the optimum values for the input variables to yield maximum SW. The best-fitted network on input data to predict SW in Japanese quails was determined with 7 neurons in the input layer, 11 neurons in the hidden layer, and one neuron in the output layer. The coefficient of determination (R
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