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使用机器学习预测Murrah水牛的性能特征:一种比较方法.

Rakesh Nehra1, Yogesh C Bangar2, C S Patil1

  • 1Department of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.

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概括
此摘要是机器生成的。

机器学习模型,包括随机森林 (RF) 和支持矢量机器 (SVM),准确预测Murrah水牛的牛奶产量. 这些算法为增强水牛繁殖计划提供了宝贵的工具.

关键词:
机器学习是机器学习.牛奶生产 牛奶生产穆拉水牛是马拉的水牛.预测 预测 预测

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科学领域:

  • 动物科学动物科学
  • 机器学习 机器学习
  • 量化遗传学 量化遗传学

背景情况:

  • 准确预测牛奶产量对于有效的畜牧管理和育种计划至关重要.
  • 穆拉水牛是重要的乳制品动物,优化它们的生产特征具有重要的经济意义.
  • 机器学习 (ML) 为分析复杂的生物数据提供了先进的计算方法.

研究的目的:

  • 评估和比较9个ML算法的性能,用于预测Murrah水牛的305天第一次哺乳乳乳产量 (305FLMY) 和总乳产量 (TMY).
  • 在水牛种群中确定最有效的ML模型用于基因改进策略.
  • 评估各种输入变量的实用性,包括测试日牛奶产量,用于预测建模.

主要方法:

  • 利用了657只Murrah水牛的数据集,记录跨越了24年的时间 (2000-2023年).
  • 输入特征包括动物细节,分娩年,第一次分娩的年龄,峰值产量,峰值产量前几天和测试日的牛奶产量 (TD1,TD2,TD3).
  • 与九个ML算法进行比较:ANN,BR,GP,GBM,MARS,MLR,RF,SMOreg和SVM,使用R2,RMSE,MAE,MAPE和偏差来评估性能.

主要成果:

  • 随机森林 (RF) 模型在预测305FLMY (R2 = 78.43%,MAPE = 9.46%) 中表现优异.
  • 支持向量机 (SVM) 模型实现了TMY的最佳预测准确性 (R2 = 71.76%,MAPE = 276.13%).
  • 人工神经网络 (ANN) 对于两种牛奶产量特征的预测性能表现最差.

结论:

  • 射频和SVM模型显示了在Murrah水牛中准确预测复杂的牛奶生产特征的巨大潜力.
  • 这些ML方法可以整合到水牛繁殖计划中,以促进遗传选择和提高群体生产力.
  • 未来的研究应该专注于改善模型可解释性和计算效率,以实现实际的农场应用.