Sivapirunthep, Panneepa

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Preferred name
Sivapirunthep, Panneepa
Alternative Name
Sivapirunthep, P.
Main Affiliation
Email
panneepa.si@kmitl.ac.th

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    Item type:Publication,
    Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks
    (2025-01-01)
    Jainonthee, Chalita
    ;
    Sanwisate, Phutsadee
    ;
    Sivapirunthep, Panneepa
    ;
    Chaosap, Chanporn
    ;
    Mektrirat, Raktham
    Dead on arrival (DOA) refers to animals, particularly poultry, that die during the pre-slaughter phase. Elevated rates of DOA frequently signify substandard welfare conditions and might stem from multiple causes, resulting in diminished productivity and economic losses. This study included 18,643 truckload entries from 45 farms, encompassing a total of 23,191,809 meat-type ducks sent to a single slaughterhouse in Eastern Thailand between January 2019 and December 2023. The objective of this study was twofold: first, to classify high DOA rates (≥ 0.15%) using several predictors, including season, period of the day, number of ducks per truckload, distance, duration of transportation, age, average body weight, lairage time, and temperature at the lairage area. This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). Additionally, several data-sampling techniques, including oversampling, undersampling, Random Over-Sampling Examples (ROSE), and Synthetic Minority Over-sampling Technique (SMOTE), were utilized to address the issue of imbalanced data. Second, to analyze variable importance contributing to the predictive outcomes. The descriptive analysis revealed a mean DOA percentage of 0.14% (range: 0 to 22.46%, SD = 0.49). The results of the high DOA classification indicated that among all models, XGBoost-Up, XGBoost-Down, and RF-Down were the top three models, achieving the highest overall scores in evaluation metrics including Area Under the ROC Curve (AUC), sensitivity, precision, and F1-score. The primary factors contributing to the high predictive performance of the models were the number of ducks per truckload, temperature at the lairage area, and average body weight. Additionally, the duration and distance of transportation, as well as the period of transportation, were secondary factors contributing to the outcome. These factors should be further investigated to minimize losses during the pre-slaughter phase in meat-type ducks. Additionally, considering these factors when managing transportation can help create conditions that reduce duck deaths.
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    Item type:Publication,
    Predicting and explaining high dead-on-arrival outcomes in meat-type ducks using deep learning: A path to improved welfare management
    (2025-09-01)
    Jainonthee, Chalita
    ;
    Sanwisate, Phutsadee
    ;
    Sivapirunthep, Panneepa
    ;
    Chaosap, Chanporn
    ;
    Pichpol, Duangporn
    Dead-on-arrival (DOA) rates are a critical welfare and economic concern in poultry production, reflecting the cumulative impact of handling, transport, and lairage conditions on bird mortality. Compared to broilers and layers, meat-type ducks have received less attention in DOA research, despite their distinct physiological responses to preslaughter stressors and increasing relevance in commercial poultry production. Although machine learning models have been widely applied for DOA prediction, their limited transparency can hinder practical application in real-world settings. This study analyzed 8220 truckload entries of meat-type ducks recorded between 2022 and 2023, with the objective of developing an explainable deep learning model to predict high DOA outcomes using preslaughter management and environmental data. Deep learning models, owing to their complex architecture, offer superior predictive capacity and can capture nonlinear interactions in high-dimensional datasets. To enhance model interpretability and support practical application, SHapley Additive exPlanations (SHAP) was applied to identify the most influential predictors of DOA classification. The final model demonstrated strong classification performance, with an accuracy of 80.29 %, precision of 79.25 %, recall of 80.29 %, F1-score of 79.66 %, and an AUC-ROC of 76.03 %. Key predictors of high DOA included duck head count, lairage temperature, duck age, and transport duration. Notably, a higher number of ducks per truckload was strongly associated with elevated DOA risk (i.e., truckloads classified in the high DOA group), along with lairage temperatures and duck ages below the respective medians. Additionally, shorter transport durations were linked to increased mortality, highlighting the complex interplay of preslaughter stressors. By leveraging SHAP analysis, this study provided both global and local interpretability, ensuring that model outputs were not only accurate but also explainable. These findings support precision-driven preslaughter interventions, enabling industry stakeholders to optimize handling, transport, and lairage practices to reduce mortality rates and enhance duck welfare.