English(EN) Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems

混合机器学习框架预测放牧系统中的牛增重

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-07 04:00

研究人员开发了一个混合机器学习框架，用于预测放牧系统中的牛增重和生长模式。该框架整合了各种传感数据，包括活重、人口统计学和环境因素，以预测牛群水平的轨迹。结合梯度提升、随机森林和神经网络的级联架构表现出卓越的性能，R^2 达到 0.889，并且优于传统的循环模型，尤其是在数据稀疏的情况下。 AI

影响该框架通过提供更准确的增重预测，可以改善牲畜管理决策。

排序理由详细介绍新机器学习框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath, Md Zahidul Islam, David W. Lamb · 2026-08-07 04:00

混合机器学习框架用于放牧式生产系统中牛群生长模式和体重增长预测

arXiv:2608.06001v1 Announce Type: new Abstract: Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd level cattle weight forecasting using automated sensing …