中国畜禽种业2026，Vol.22Issue(7)：7-16,10.DOI:10.19543/j.cnki.1673-4556.20260522.002
畜禽重要表型智能测定技术研究进展与展望
Research progress and prospects of intelligent measurement technologies for important phenotypes in livestock and poultry
摘要
Abstract
With the rapid development of modern seed industry and intelligent livestock farming,the phenotyping of important traits in livestock and poultry is transitioning from traditional manual measurement towards automated,intelligent,and high-throughput approaches.This review focuses on livestock such as pigs and poultry,systematically summarizing research progress in intelligent phenotyping technologies for key traits including growth,body measurements,physiological parameters,and behavior.It discusses the principles and application status of core technologies such as computer vision,laser radar,wearable and implantable sensors,analyzes AI-based data processing methods and their value in genetic evaluation,compares the development levels of intelligent phenotyping technology for livestock and poultry domestically and internationally,examines challenges in technology application,and prospects future directions such as multi-omics integration and intelligent breeding,this initiative systematically integrates multidisciplinary technological achievements,clarifies technical application pain points and breakthrough pathways,with a focus on core technological advantages like non-contact measurement and dynamic physiological monitoring,while balancing theoretical depth and industrial applicability.It aims to accelerate the transformation of China's livestock industry toward efficiency,green practices,and intelligence,ensuring the supply security of livestock and poultry products,and strengthening the technical foundation for enhancing the core competitiveness of the seed industry.关键词
表型/智能测定/计算机视觉/传感器/人工智能/畜禽育种Key words
Phenotypic/Intelligent measurement/Computer vision/Sensors/Artificial intelligence/Livestock and poultry breeding分类
农业科技引用本文复制引用
程万里,杨明宇,席磊,李转见,郭玉洁..畜禽重要表型智能测定技术研究进展与展望[J].中国畜禽种业,2026,22(7):7-16,10.基金项目
国家重点研发计划子课题(2023YFD1300804-2). （2023YFD1300804-2）
