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This research aims to evaluate the performance of different machine learning algorithms for predicting chicken body weight based on genotype and growth rate.

February 22, 2026Tropical Animal Health and ProductionOpen Access

AI-Driven prediction of body weight in chicken genotypes with different growth rates

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This research aims to evaluate the performance of different machine learning algorithms for predicting chicken body weight based on genotype and growth rate.
Evaluated four machine learning algorithms: GER, FFNN, SVM, DT.
Conducted a controlled experiment with 300 male chicks from three breeds: SAGA, Sasso, and Cobb 500.
Collected weekly body weight and six morphometric traits over four weeks.
Split data into training (75%) and validation (25%) sets.
GER consistently outperformed other models in accuracy and error rates.
FFNN was effective for slow and medium breeds but struggled with fast-growing Cobb 500.
SVM and DT had the lowest performance with higher error rates.
Accuracy ranking: GER > FFNN > DT/SVM.

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Analysis evaluates machine learning algorithms predicting body weight in chickens, indicating which models perform best.

Cite This Study

Musa et al. (2026) studied this question.

synapsesocial.com/papers/699a9d8e482488d673cd3762 https://doi.org/10.1007/s11250-026-04925-x

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AI-Driven prediction of body weight in chicken genotypes with different growth rates

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Implication

Cite This Study

AI-Driven prediction of body weight in chicken genotypes with different growth rates

Key Points

Discussion

Implication

Cite This Study

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