ArticleScientific reports2022
Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Body Weight Prediction in Karayaka Lambs Using Morphometric Measurements: A Comparison of Regression and Machine Learning Approaches.Animals : an open access journal from MDPI · 2026Article
- Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.Tropical animal health and production · 2026Article
- KNLR: A heterogeneous ensemble learner for predicting Foie gras weight grade in mule ducks (Anas platyrhynchos × Cairina moschata).Poultry science · 2026Article
- From Sensors to Sustainability: Integrating Welfare, Management, and Climate Resilience in Small Ruminant Farm Systems.Animals : an open access journal from MDPI · 2025Review
- Early prediction of final weight in nomadic sheep flocks using machine learning algorithms.Tropical animal health and production · 2025Article
- Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows.Animals : an open access journal from MDPI · 2025Article
- Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs.Animals : an open access journal from MDPI · 2025Article
- Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms.Archives animal breeding · 2025Article
- Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep.Frontiers in genetics · 2025Article
- Effective Strategies for Understanding Meat Flavor: A Review.Food science of animal resources · 2025Review
- Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.Tropical animal health and production · 2024Article
- Artificial intelligence algorithm comparison and ranking for weight prediction in sheep.Scientific reports · 2023Article
- Development of a multi-use decision support system for scientific management and breeding of sheep.Scientific reports · 2022Article
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Authors and funding
6 authors.
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Abstract
As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 12 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through principal component analysis (PCA) was 1. The correlation coefficients between true and predicted values for artificial neural networks, Bayesian ridge regression, classification and regression trees, gradient boosting algorithm, K nearest neighbours, multivariate adaptive regression splines (MARS) algorithm, polynomial regression, principal component regression (PCR), random forests, support vector machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. Among the prediction equations generated using OLS, the highest coefficient of determination was 0.569. A total of 12 machine learning models were developed from the prediction of breeding values in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies for breeding value prediction.
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