中国畜禽种业 ›› 2026, Vol. 22 ›› Issue (7): 7-16.doi: 10.19543/j.cnki.1673-4556.20260522.002cstr: 32418.14.j.cnki.1673-4556.20260522.002

• 前沿技术 •    下一篇

畜禽重要表型智能测定技术研究进展与展望

程万里1, 杨明宇1, 席磊2, 李转见1, 郭玉洁1   

  1. 1. 河南农业大学动物科技学院，河南 郑州 450046
    2. 河南牧业经济学院，河南 郑州 450046
  • 收稿日期:2025-12-26 出版日期:2026-07-26 发布日期:2026-07-18
  • 作者简介:
    程万里（2002—），男，河南农业大学，硕士研究生，河南南阳人，研究方向：家禽遗传育种，E-mail：。
    李转见（1985—），男，河南鹿邑人，研究方向：鸡优异性状遗传机制解析与生物育种，河南农业大学，博士，二级教授，博士生导师，国家级青年人才、农业农村部神农青年英才、国家蛋鸡产业技术体系岗位专家、强国青年科学家、第八届井冈新秀、中原科技领军人才、中原青年拔尖人才、河南省高校科技创新人才等；兼任中国畜牧兽医学会家禽学分会常务理事、畜禽遗传标记学分会常务理事、遗传育种学分会理事等；兼任《中国畜禽种业》副主编、《Animal Research and One Health》编委、《中国家禽》和《农业科学研究》首届青年编委等。E-mail：
    郭玉洁（1991—），女，河南郑州人，研究方向：家禽遗传育种，地方鸡优异基因挖掘与改良利用，河南农业大学，博士，高级实验师，硕士研究生导师，兼任《中国畜禽种业》青年编委等。主持国家自然科学基金青年项目、河南省高等学校重点科研项目计划、农业农村部畜禽资源（家禽）评价利用重点实验室开放课题等，荣获地方鸡主要经济性状精准分子选育技术体系创建与应用，河南省科学技术进步奖一等奖，河南农业大学地方鸡保护利用创新团队，神农中华农业科技奖等。E-mail：。
  • 基金资助:
    国家重点研发计划子课题(2023YFD1300804-2)

Research progress and prospects of intelligent measurement technologies for important phenotypes in livestock and poultry

Wanli Cheng1, Mingyu Yang1, Lei Xi2, Zhuanjian Li1, Yujie Guo1   

  1. 1. College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450000, Henan
    2. Henan University of Animal Husbandry and Economy, Zhengzhou, 450046, Henan
  • Received:2025-12-26 Online:2026-07-26 Published:2026-07-18

RichHTML

5

PDF (PC)

3

摘要：

现代种业与智慧养殖快速发展，畜禽重要表型测定正从传统人工测量向自动化、智能化、高通量测定方向转变。本文聚焦猪、禽等畜禽，系统梳理生长、体尺、生理及行为等重要表型智能测定技术的研究进展，探讨计算机视觉、激光雷达、穿戴式与植入式传感器等核心技术的原理及应用现状，分析基于人工智能的数据处理方法及其在遗传评估中的应用价值，对比国内外畜禽表型智能测定技术的发展水平，剖析技术应用面临的挑战，展望多组学融合、智能化育种等未来发展方向，系统整合多学科技术成果，明晰技术应用痛点与突破路径，重点突出非接触式测定、动态生理监测等核心技术优势，兼顾理论深度与产业实用性，以期为加速我国畜牧产业向高效、绿色、智能转型，保障畜禽产品供给安全、提升种业核心竞争力筑牢技术支撑。

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

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.

Key words: Phenotypic, Intelligent measurement, Computer vision, Sensors, Artificial intelligence, Livestock and poultry breeding

中图分类号: 

  • S81

图1

CIEN-LWEN框架示意图［9］"

图2

基于激光雷达和点云分割模型的猪体尺测量方法示意图［2］"

图3

生物舒适的自张紧穿戴式鸡体温计［20］"

图4

背包式可穿戴传感器［22］"

图5

RFID的可穿戴脚环和智能巢箱［24］"

图6

牛尾跟踪与轨迹分析示意图［37］"

[1]
LIU L, YI G Q, YAO Y L, et al. Multiomics analysis reveals signatures of selection and loci associated with complex traits in pigs[J]. iMeta, 2024, 3(6): e250.
[2]
JIANG Y H, LI Z C, CAO J S, et al. Body parts segmentation and phenotypic traits extraction of pig using an improved point cloud segmentation network with multi-LiDAR[J]. Computers and Electronics in Agriculture, 2025, 110624-110624.
[3]
冯丽妃. 智能化养猪助力育种领域全新探索[N]. 中国科学报，2025.
FENG F L. Intelligent pig farming facilitates novel exploration in breeding field[N]. China Science Daily, 2025
[4]
BERCKMANS D. Precision livestock farming technologies for welfare management in intensive livestock systems[J]. Revue scientifique et technique (International Office of Epizootics), 2014, 33(1): 189-96.
[5]
赵春江. 智慧农业的发展现状与未来展望[J]. 科学大观园, 2024(9): 30-37.
ZHAO C J. The Development status and future prospect of smart agriculture[J]. The Grand Garden of Science, 2024(9): 30-37.
[6]
WU J, YANG Y Y, YANG W, et al. Non-destructive and efficient prediction of intramuscular fat in live pigs based on ultrasound images and machine learning[J]. Computers and Electronics in Agriculture, 2025, 234: 110291.
[7]
LU H X, ZHANG J L, GUO H, et al. Automatic coarse-to-fine method for cattle body measurement based on improved GCN and 3D parametric model[J]. Computers and Electronics in Agriculture, 2025, 231: 110017.
[8]
张慧林, 燕凤, 刘小林, 等. 仔猪哺乳期的生长发育分析[J]. 中国农学通报, 2007, 23(9): 44-47.
ZHANG H L, YAN F, LIU X L, et al. Growth analysis of piglet at lactation period[J]. Chinese Agricultural Science Bulletin, 2007, 23(9): 44-47.
[9]
DONG X M, ZHANG C M, WANG P Y, et al. A novel dual-network approach for real-time liveweight estimation in precision livestock management[J]. Advanced Science, 2025, 12(22): 2417682.
[10]
FERNANDES A F A, DÓREA J R R, ROSA G J M. Image analysis and computer vision applications in animal sciences: an overview[J]. Frontiers in Veterinary Science, 2020, 7: 551269.
[11]
KASHIHA M, BAHR C, OTT S, et al. Automatic weight estimation of individual pigs using image analysis[C]. //Computers and Electronics in Agriculture. ACM, 2014: 38-44.
[12]
张婷, 李琦. 基于三维点云的牛体尺测量研究[J]. 黑龙江畜牧兽医, 2023(3): 58-61.
ZHANG T, LI Q. Research on cattle body size measurement based on three-dimensional point cloud[J]. Heilongjiang Animal Science and Veterinary Medicine, 2023(3): 58-61.
[13]
谷志帅, 蔡振江, 程曼, 等. 基于激光雷达的小尾寒羊体尺快速获取方法[J]. 黑龙江畜牧兽医, 2025(1): 52-58.
GU Z S, CAI Z J, CHENG M, et al. Fast method of acquiring body size of short-tail Han sheep based on lidar[J]. Heilongjiang Animal Science and Veterinary Medicine, 2025(1): 52-58.
[14]
WANG Y W, KOOISTRA L, MÜCHER S, et al. Integrated unmanned aerial vehicle-based LiDAR and RGB data for individual cattle growth monitoring in precision livestock farming[J]. Scientific Data, 2025, 12: 1318.
[15]
潘芷欣, 陈荟慧, 钟委钊. 基于可穿戴设备的母猪产后行为识别[J]. 农业工程学报, 2022, 38(S1): 209-217.
PAN Z X, CHEN H H, ZHONG W Z. Recognition of the behaviour of lactating sows using wearable devices[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(S1): 209-217.
[16]
穆元杰, 尚明华, 郑纪业, 等. 畜禽体温自动监测技术与应用综述[J]. 农业工程学报, 2025, 41(1): 40-54.
MU Y J, SHANG M H, ZHENG J Y, et al. Review of automatic temperature monitoring technologies and their applications in livestock and poultry[J]. Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(1): 40-54.
[17]
GU H J, LI J, GU Z, et al. Design of standardized breeding system for rabbits based on internet of things[C]. // Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing. 2016: 74-77.
[18]
田浩楠, 华婧伊, 张少帅, 等. 基于红外热成像与线性回归拟合的母猪体温检测技术研究[J]. 智能化农业装备学报(中英文), 2023, 4(1): 36-41.
TIAN H N, HUA J Y, ZHANG S S, et al. Research on the measurement of sow body temperature based on infrared thermography and linear regression fitting[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 36-41.
[19]
岡田浩尚, 伊藤寿浩, 鈴木耕太郎, 等. 無線センサーネットワークを用いた鳥インフルエンザ早期発見システムの開発[C]. // 精密工学会学術講演会講演論文集, 2009, 2009A(0): 445-446.
OKADA H, ITO H, SUZUKI K, et al. Development of an Early Detection System for Avian Influenza Using Wireless Sensor Networks[C]. //Proceedings of the Japan Society of Precision Engineering, 2009, 2009A(0): 445-446.
[20]
杨威. 蛋鸡穿戴式无线体温感知设备的开发及体温监测实验研究[D]. 杭州: 浙江大学, 2017.
YANG W. Development of wearable wireless temperature sensing device for layer hens and experimental study on temperature monitoring[D]. Hangzhou: Zhejiang University, 2017.
[21]
罗洁, 高函雅, 刘丹丹. 实验动物福利研究进展[J]. 当代畜牧, 2025(8): 138-140.
LUO J, GAO H Y, LIU D D. Research progress on welfare of experimental animals[J]. Contemporary Animal Husbandry, 2025(8): 138-140.
[22]
DERAKHSHANI S M, OVERDUIN M, VAN NIEKERK T G C M, et al. Implementation of inertia sensor and machine learning technologies for analyzing the behavior of individual laying hens[J]. Animals, 2022, 12(5): 536.
[23]
尹令, 洪添胜, 刘迎湖, 等. 基于无线传感器网络支持向量机奶牛行为特征识别[J]. 传感技术学报, 2011, 24(3): 458-462.
YIN L, HONG T S, LIU Y H, et al. Cow behavioral features recognition using binary decision tree support vector machines based on wireless sensor network[J]. Chinese Journal of Sensors and Actuators, 2011, 24(3): 458-462.
[24]
CHIEN Y R, CHEN Y X. An RFID-based smart nest box: an experimental study of laying performance and behavior of individual hens[J]. Sensors, 2018, 18(3): 859.
[25]
TU X Y, DU S X, TANG L, et al. A real-time automated system for monitoring individual feed intake and body weight of group housed turkeys[J]. Computers and Electronics in Agriculture, 2011, 75(2): 313-320.
[26]
GUMUS A, LEE S, AHSAN S S, et al. Lab-on-a-bird: biophysical monitoring of flying birds[J]. PLoS One, 2015, 10(4): e0123947.
[27]
许鑫, 马文政, 张浩, 等. 基于融合对抗训练的农作物品种信息抽取方法[J]. 农业机械学报, 2023, 54(12): 272-279, 337.
XU X, MA W Z, ZHANG H, et al. Crop variety information extraction method based on integrated adversarial training[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(12): 272-279, 337.
[28]
熊本海, 蒋林树, 杨亮, 等. 种猪生产性能测定系统开发与性能测试[J]. 农业工程学报, 2017, 33(9): 174-179.
XIONG B H, JIANG L S, YANG L, et al. Design and performance testing of production performance determination system for boar[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(9): 174-179.
[29]
RADOGLOU-GRAMMATIKIS P, SARIGIANNIDIS P, LAGKAS T, et al. A compilation of UAV applications for precision agriculture[J]. Computer Networks, 2020, 172: 107148.
[30]
SCHILLINGS J, BENNETT R, ROSE D C. Exploring the potential of precision livestock farming technologies to help address farm animal welfare[J]. Frontiers in Animal Science, 2021, 2: 639678.
[31]
陆杰, 李双智, 张宝羽, 等. 种猪生长性状校正公式研究[J]. 中国猪业, 2024, 19(3): 77-84.
LU J, LI S Z, ZHANG B Y, et al. Research on the correction formula for production traits of breeding swines[J]. China Swine Industry, 2024, 19(3): 77-84.
[32]
刘望宏. 瘦肉型种猪早期生长性状校正公式及其效果初步研究[D]. 武汉: 华中农业大学, 2007.
LIU W H. A preliminary study on the correction formula of early growth traits of lean-type boar[D]. Wuhan: Huazhong Agricultural University, 2007.
[33]
朱芷芫, 王海峰, 李斌, 等. 深度学习在畜禽典型行为识别中的研究进展[J]. 中国农业科技导报, 2024, 26(10): 110-124.
ZHU Z Y, WANG H F, LI B, et al. Research progress of deep learning in typical behavior recognition of livestock and poultry[J]. Journal of Agricultural Science and Technology, 2024, 26(10): 110-124.
[34]
张宏鸣, 李永恒, 周利香, 等. 基于改进YOLO v3的肉牛多目标骨架提取方法[J]. 农业机械学报, 2022, 53(3): 285-293.
ZHANG H M, LI Y H, ZHOU L X, et al. Multi-target skeleton extraction method of beef cattle based on improved YOLO v3[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(3): 285-293.
[35]
赵凯旋, 刘晓航, 姬江涛. 基于EfficientNet与点云凸包特征的奶牛体况自动评分[J]. 农业机械学报, 2021, 52(5): 192-201, 73.
ZHAO K X, LIU X H, JI J T. Automatic body condition scoring method for dairy cows based on EfficientNet and convex hull feature of point cloud[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(5): 192-201, 73.
[36]
KAMILARIS A, PRENAFETA-BOLDÚ F X. Deep learning in agriculture: a survey[J]. Computers and Electronics in Agriculture, 2018, 147: 70-90.
[37]
LI J W, ZENG P B, YUE S, et al. Automatic body condition scoring system for dairy cows in group state based on improved YOLOv5 and video analysis[J]. Artificial Intelligence in Agriculture, 2025, 15(2): 350-362.
[38]
ADAK A, KANG M, ANDERSON S L, et al. Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data[J]. Journal of Experimental Botany, 2023, 74(17): 5307-5326.
[39]
张丽飞, 张铭珠, 王新乐, 等. 家畜遗传评定方法的研究进展[J]. 中国畜牧杂志, 2025, 61(4): 31-41.
ZHANG L F, ZHANG M Z, WANG X L, et al. Research progress on livestock genetic evaluation methods[J]. Chinese Journal of Animal Science, 2025, 61(4): 31-41.
[40]
赵孝凤, 郭刚, 曹杰, 等. 中国荷斯坦牛健康性状遗传参数估计[J]. 中国畜牧兽医, 2024, 51(6): 2501-2507.
ZHAO X F, GUO G, CAO J, et al. Estimation of genetic parameters for health traits in Chinese Holstein[J]. China Animal Husbandry & Veterinary Medicine, 2024, 51(6): 2501-2507.
[41]
ABDEL-WARETH A A A, AHMED A A, SALAHUDDIN M, et al. Application of artificial intelligence in rabbit husbandry: from reproductive monitoring to precision farming[J]. Frontiers in Veterinary Science, 2025, 12: 1679630.
[42]
YANG G Y, XU X S, SONG L, et al. Automated measurement of dairy cows body size via 3D point cloud data analysis[J]. Computers and Electronics in Agriculture, 2022, 200: 107218.
[43]
KLUKAS C, CHEN D J, PAPE J M. Integrated analysis platform: an open-source information system for high-throughput plant phenotyping[J]. Plant Physiology, 2014, 165(2): 506-518.
[44]
CHEN D J, NEUMANN K, FRIEDEL S, et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis[J]. The Plant Cell, 2014, 26(12): 4636-4655.
[45]
TULLO E, FINZI A, GUARINO M. Review: environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy[J]. Science of the Total Environment, 2019, 650: 2751-2760.
[46]
郭玉麟, 胡以华, 方佳节, 等. 激光雷达超分辨成像技术研究现状与发展趋势[J]. 雷达学报(中英文), 2025, 14(3): 501-527.
GUO Y L, HU Y H, FANG J J, et al. Research status and development trend of LiDAR super-resolution imaging technology[J]. Journal of Radars, 2025, 14(3): 501-527.
[1] 王清正, 杨娜, 王惠娥, 党瑞华, 依那木江·亚力买买提null, 木巴热克·木亥麦特null, 艾尔肯·阿力木null, 图尔迪巴柯·图尔荪托合提null, 赵天心, 努日克亚汗·艾力null, 格明古丽·木哈台null. 吐鲁番斗鸡杂化现状及品种保护建议[J]. 中国畜禽种业, 2026, 22(5): 116-124.
[2] 何豫涵, 樊红灯, 李凯, 王龙威, 白俊艳, 张俊. 浅析肉牛重要经济性状的精准鉴定技术[J]. 中国畜禽种业, 2023, 19(7): 56-60.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
主管：中华人民共和国农业农村部
主办：中国农业科学院农业信息研究所
ISSN 1673-4556
CN 11-5342/S
邮发代号：80-222
地址：北京市海淀区中关村南大街12号
邮编：100081
电话：010-82106253(兼传真)　82106255
E-mail：xqzy@caas.cn

版权所有 © 《中国畜禽种业》编辑部