中国畜牧杂志2025，Vol.61Issue(4)：1-6,6.DOI:10.19556/j.0258-7033.20240522-05
基于机器学习预测奶牛乳成分、代谢及健康性状的研究进展
Research Progress on Predicting Milk Composition,Metabolism and Health Traits of Dairy Cows by Machine Learning
摘要
Abstract
With the advancement of technology,sensors and non-invasive tools have provided new ways to economically and efficiently obtain high-throughput phenotypic data of dairy cows,while also presenting challenges in big data processing and analysis.Infrared spectroscopy,due to its non-destructive,high-throughput,simple,rapid,and low-cost characteristics,is widely used in measuring dairy cow production performance,generating large,usable mid-infrared spectroscopy datasets.These spectral data can be used to predict dairy cow milk composition information,and further predict cow metabolic and health traits;however,the accuracy and robustness of the models still need improvement.Machine learning,with its ability for intelligent prediction and pattern recognition of large datasets,shows great potential in addressing these challenges.Using machine learning algorithms to process and analyze infrared spectroscopy data can extract valuable information from complex datasets,enhancing the robustness and accuracy of prediction models.This paper reviews infrared spectroscopy technology,machine learning,and their applications in predicting dairy cow milk composition,metabolic,and health traits,providing new research perspectives and practical strategies for the development of precision livestock farming.关键词
表型/红外光谱/机器学习/奶牛Key words
Phenotype/Infrared spectroscopy/Machine learning/Dairy cows分类
农业科技引用本文复制引用
戴雨池,韩博,陈傲,郑伟杰,家瑞科,叶雯,王哲,曹慧,贺巾锋,孙东晓..基于机器学习预测奶牛乳成分、代谢及健康性状的研究进展[J].中国畜牧杂志,2025,61(4):1-6,6.基金项目
国家重点研发计划(2021YFF1000700) （2021YFF1000700）
科技创新2030—重大项目(2023ZD04069) （2023ZD04069）
长江学者和创新团队发展计划(IRT_15R62) （IRT_15R62）
国家现代农业产业技术体系(CARS-36) （CARS-36）
中国农业大学动物科学技术学院青年英才发展计划 （）
