Doctoral Program in Innovative Tropical Agriculture, Department of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Department of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Poult Sci. 2024 Apr;103(4):103504. doi: 10.1016/j.psj.2024.103504. Epub 2024 Jan 30.
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.
了解宰前处理过程中死亡即到（DOA）事件的因素对于明智决策、改善肉鸡福利和优化农场盈利能力至关重要。在这项研究中，使用了 3 种不同的机器学习（ML）算法——最小绝对收缩和选择算子（LASSO）、分类树（CT）和随机森林（RF），并结合 4 种抽样技术来优化不平衡数据。该数据集来自泰国一家大型生产商的 22115 个肉鸡运输卡车（2021-2022 年），包含 14 个独立变量，涵盖饲养、捕捉和运输阶段。研究重点是 DOA%在 0.10 到 1.20%之间，高 DOA%的阈值高于 0.3%，并记录宰前活体检查期间每辆卡车的 DOA%。由于高 DOA 率为 25.2%，不平衡数据集促使实施 4 种方法来调整不平衡参数：随机过采样（ROS）、随机欠采样（RUS）、两种采样（BOTH）和合成采样或随机过采样示例（ROSE）。目的是提高预测模型在分类和预测高 DOA%方面的性能。不同误差指标的比较分析表明，RF 在平衡数据集中的表现优于其他模型。特别是，与原始不平衡数据集相比，RUS 在所有模型中的预测性能都有显著提高。识别出预测高 DOA%百分比的 4 个最重要变量——死亡率和淘汰率、饲养密度、季节和平均体重——强调了它们对肉鸡生产的重要性。本研究为使用 ML 方法预测 DOA 状态提供了有价值的见解，并为商业肉鸡生产中减轻高 DOA%百分比的更有效策略的发展做出了贡献。
