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使用机器学习算法预测肉中伤的建模

Pranee Pirompud¹, Panneepa Sivapirunthep², Veerasak Punyapornwithaya³

¹Doctoral 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.

Poultry science

|September 3, 2025

概括

机器学习模型可以预测肉的伤. 极端梯度提升 (XGB) 算法在识别高伤风险,帮助福利和经济结果方面表现最好.

科学领域:

动物科学
农业工程
数据科学

背景情况:

肉的伤是家禽生产的一个重要问题,对动物福利和经济回报产生了负面影响.
在养殖,捕捞,运输和屠宰过程中的身体压力和伤害导致了尸体的伤.

研究的目的:

为了分类肉的伤风险 (低或高百分比的尸体伤) 每辆卡车.
为了比较六种机器学习模型的预测性能,
提供决策工具以加强福利监测和减少经济损失.

主要方法:

评估了六种机器学习模型:LASSO,CT,RF,NB,SVM和XGB.
使用了26,031辆卡车的数据集,其中包括从养到屠宰阶段的14个预测因素.
在没有重新采样的情况下训练模型,因为自然分布的伤率很高 (41.4%的卡车载荷).

主要成果:

与其他五种模型相比,极度梯度提升 (XGB) 算法显示出优异的预测性能.
伤的关键预测因素包括平均体重,运输时间,养密度 (和箱子),运输距离,死亡率/屠宰率,养时间和养时间.
整体预测准确度中等,可能是由于缺少关键变量.

结论:

机器学习,特别是XGB,是早期识别容易受伤害的肉群的宝贵工具.
有针对性的管理干预措施,重点关注鸟类的健康,养密度,养时间和息时间,可以有效地缓解伤.
实施这些战略可以显著提高商业肉生产的动物福利和运营效率.

关键词:

极端梯度增强藏时间模型性能重要性的变量

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