Machine learning predictive modeling for condemnation risk assessment in antibiotic-free raised broilers.

ABSTRACT: The condemnation of broiler carcasses in the poultry industry is a major challenge and leads to significant financial losses and food waste. This study addresses the critical issue of condemnation risk assessment in the discarding of antibiotic-free raised broilers using machine learning (ML) predictive modeling. In this study, ML approaches, specifically least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forests (RF), were used to evaluate and compare their effectiveness in predicting high condemnation rates. The dataset of 23,959 truckloads from 2021 to 2022 contained 14 independent variables covering the rearing, catching, transportation, and slaughtering phases. Condemnation rates between 0.26% and 25.99% were used as the dependent variable fo

SUBMITTER: Pirompud P 

PROVIDER: S-EPMC11415764 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature