根据使用LightGBM,XGBoost和支持矢量机算法的身体测量,预测肉类交叉的雌性子的活体重
Hasan Önder1, Cem Tirink2, Taras Yakubets3
1Faculty of Agriculture, Department of Animal Science, Ondokuz Mayis University, Samsun, Türkiye.
Veterinary medicine and science
|January 10, 2025
概括
预测子的体重 (BW) 使用生物识别测量,如身体长度 (BL) 对于动物福利至关重要. 机器学习模型在BW预测中实现了超过95%的准确性,其中BL是最重要的因素.
科学领域:
- 动物科学动物科学
- 机器学习 机器学习
- 生物技术是生物技术.
背景情况:
- 准确的体重 (BW) 预测对于动物福利和自动化表型化至关重要.
- 数学模型对于畜牧业可靠的BW估计至关重要.
- 生物识别测量为评估动物生长和健康提供了一种非侵入性方法.
研究的目的:
- 开发和比较机器学习模型来预测子的体重 (BW).
- 评估身体长度 (BL),胸围 (CG) 和腰宽 (WW) 作为BW预测指标的有效性.
- 确定在Hyla NG子中对BW预测最有影响力的生物识别变量.
主要方法:
- 使用LightGBM,XGBoost和支持矢量机 (SVM) 算法进行BW预测.
- 使用生物识别数据训练和测试模型,包括BL,CG和WW.
- 使用确定系数,RMSE和MAE评估模型性能.
主要成果:
- 在训练和测试数据集上,LightGBM,XGBoost和SVM模型对于BW预测具有很高的准确性 (超过95%).
- 所有评估的算法都显示出非常适合从生物识别测量中预测BW.
- 身体长度 (BL) 被确定为子BW最重要的解释变量.
结论:
- 机器学习算法,特别是LightGBM,XGBoost和SVM,对于使用生物识别数据预测子BW非常有效.
- 生物识别测量,特别是身体长度,为非侵入性BW估计提供了可靠的基础.
- 这些发现支持在养殖中应用自动化表型和改善动物福利实践.
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