English(EN) A physics-informed, behavior-aware digital twin for robust multimodal forecasting of core body temperature in precision livestock farming

人工智能驱动的数字孪生以92%的准确率预测牛的热应激

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个用于精准畜牧业的新型数字孪生框架，专注于预测奶牛的核心体温以检测热应激。该框架整合了基于物理的模拟和机器学习模型，包括高斯过程、卡尔曼滤波器和马尔可夫链，以模拟体温调节、个体偏差、实时数据对齐和活动状态。该系统使用三阶段堆叠的LightGBM模型集成，在提前2小时的预测中实现了0.783的交叉验证R2和92.38%的预测区间覆盖率。 AI

影响该框架通过实现对牲畜热应激的早期检测，可以改善动物福利和农场管理。

排序理由该集群包含一篇详细介绍特定应用新方法和框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Riasad Alvi, Mohaimenul Azam Khan Raiaan, Sadia Sultana Chowa, Arefin Ittesafun Abian, Reem E Mohamed, Md Rafiqul Islam, Yakub Sebastian, Sheikh Izzal Azid, Sami Azam · 2026-10-27 04:00

面向精准畜牧业的物理信息、行为感知数字孪生，用于核心体温的鲁棒多模态预测

arXiv:2604.04098v3 Announce Type: replace Abstract: Precision livestock farming requires accurate and timely heat stress prediction to ensure animal welfare and optimize farm management. This study presents a physics-informed digital twin (DT) framework combined with an uncertain…

本文如何被排名

报道来源 [1]

面向精准畜牧业的物理信息、行为感知数字孪生，用于核心体温的鲁棒多模态预测

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