Sivapirunthep, Panneepa

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Sivapirunthep, Panneepa
Alternative Name
Sivapirunthep, P.
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Email
panneepa.si@kmitl.ac.th

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    Item type:Publication,
    Application of machine learning algorithms to predict dead on arrival of broiler chickens raised without antibiotic program
    (2024-04-01)
    Pirompud, Pranee
    ;
    Sivapirunthep, Panneepa
    ;
    Punyapornwithaya, Veerasak
    ;
    Chaosap, Chanporn
    Understanding the factors of dead-on-arrival (DOA) incidents during pre-slaughter handling is crucial for informed decision-making, improving broiler welfare, and optimizing farm profitability. In this study, 3 different machine learning (ML) algorithms — least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forest (RF) - were used together with 4 sampling techniques to optimize imbalanced data. The dataset comes from 22,115 broiler truckloads from a large producer in Thailand (2021–2022) and includes 14 independent variables covering the rearing, catching, and transportation stages. The study focuses on DOA% in the range of 0.10 to 1.20%, with a threshold for high DOA% above 0.3%, and records DOA% per truckload during pre-slaughter ante-mortem inspection. With a high DOA rate of 25.2%, the imbalanced dataset prompts the implementation of 4 methods to tune the imbalance parameters: random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and synthetic sampling or random over sampling example (ROSE). The aim is to improve the performance of the prediction model in classifying and predicting high DOA%. The comparative analysis of the different error metrics shows that RF outperforms the other models in a balanced dataset. In particular, RUS shows a significant improvement in prediction performance across all models compared to the original unbalanced dataset. The identification of the 4 most important variables for predicting high DOA percentages - mortality and culling rate, rearing stocking density, season, and mean body weight - emphasizes their importance for broiler production. This study provides valuable insights into the prediction of DOA status using an ML approach and contributes to the development of more effective strategies to mitigate high DOA percentages in commercial broiler production.
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    Item type:Publication,
    Machine learning predictive modeling for condemnation risk assessment in antibiotic-free raised broilers
    (2024-12-01)
    Pirompud, Pranee
    ;
    Sivapirunthep, Panneepa
    ;
    Punyapornwithaya, Veerasak
    ;
    Chaosap, Chanporn
    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 for the analysis, with the threshold for a high conviction rate set at 3.0%. As high condemnation rates were in the minority (8.05%), sampling methods such as random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and random over sampling example (ROSE) were used to account for imbalanced datasets. The results showed that RF with RUS performed better than the other models for balanced datasets. In this study, mean body weight, weight per crate, mortality and culling rates, and lairage time were identified as the 4 most important variables for predicting high condemnation rates. This study provides valuable insights into ML applications for predicting condemnation rates in antibiotic-free raised broilers and provides a framework to improve decision-making processes in establishing farm management practices to minimize economic losses in the poultry industry. The proposed methods are adaptable for different broiler producers, which increases their applicability in the industry.