肉牛绿色健康智慧养殖：框架、技术与展望
收稿日期: 2025-08-02
录用日期: 2025-10-10
网络出版日期: 2026-06-26
Green, Healthy, and Smart Beef Cattle Farming: Framework, Technologies, and Future Outlook
Received date: 2025-08-02
Accepted date: 2025-10-10
Online published: 2026-06-26
随着人工智能等新一代信息技术与畜牧养殖业的深度融合，肉牛养殖正朝着绿色、健康、智慧养殖方向转型。从肉牛养殖业务流与信息流融合的视角，以养殖全过程的“动态感知—精准管控—智能决策—应用反馈”为主线，构建肉牛绿色健康智慧养殖的整体框架，包含四个核心模块：动态感知与监测、智能管控与作业、智慧分析与决策、创新集成与示范，打造以数据驱动为核心、技术融合为支撑、智能装备为抓手、产业落地为导向的养殖体系；分析了实施该框架应具备的四项关键技术：智能感知与健康监测、生态环境绿色调控、精准饲喂与生物安全防控技术、智慧系统决策技术，总结归纳了关键技术的概念、最新进展及应用价值；从理论体系深化、关键技术迭代、应用场景适配、产业生态协同等方面，对肉牛智慧养殖的未来发展方向进行了展望，可为推进畜牧业高质量发展与现代化转型提供理论研究与实践参考。
张崧雪 , 孔繁涛 , 王越 , 曹姗姗 , 张志勇 , 孙伟 . 肉牛绿色健康智慧养殖：框架、技术与展望[J]. 农业大数据学报, 2026 , 8(2) : 199 -211 . DOI: 10.19788/j.issn.2096-6369.000126
With the deep integration of new-generation information technologies such as artificial intelligence into animal husbandry, beef cattle farming is undergoing a transformation toward green, healthy, and smart practices. From the perspective of integrating operational and informational flows, this paper constructs a comprehensive framework for smart beef cattle farming along the process of “dynamic sensing - precise control - intelligent decision-making - feedback application.” The framework comprises four core modules: dynamic sensing and monitoring, intelligent control and operations, smart analysis and decision-making, and innovative integration and demonstration. It aims to establish a farming system driven by data, supported by integrated technologies, enabled by intelligent equipment, and guided by industry applications. Four key technologies essential for implementation are analyzed: intelligent sensing and health monitoring, precision feeding and biosecurity control technology, and smart decision systems. Their concepts, recent advancements, and application values are summarized. Finally, future development directions are explored in terms of theoretical enhancement, technological iteration, scenario adaptability, and industrial ecosystem coordination, providing theoretical and practical references for the high-quality and modern transformation of animal husbandry.
| [1] | 国家统计局农村社会经济调查司. 中国农村统计年鉴 2024[M]. 北京: 中国统计出版社, 2024. |
| Rural Social and Economic Statistics Department, National Bureau of Statistics of China. China Rural Statistical Yearbook 2024[M]. Beijing: China Statistics Press, 2024. | |
| [2] | 张帆, 周梦婷, 熊本海, 等. 肉牛生理指标智能监测技术研究进展与展望. 智慧农业(中英文), 2024, 6(4): 1-17. |
| ZHANG F, ZHOU M T, XIONG B H, et al. Research advances and prospect of intelligent monitoring systems for the physiological indicators of beef cattle. Smart Agriculture (Chinese and English Edition), 2024, 6(4): 1-17. | |
| [3] | 国际畜牧科技, 国畜解读. 2025年上半年肉牛产业报告:全国牛存栏9992万头！肉牛出栏2203万头！牛肉产量342万吨...[EB/OL]. (2025-07-18)[2025-07-24]. https://mp.weixin.qq.com/s/PfQYoWvbbvC-o-zsQYnWtA. |
| International Livestock Science; Guochu Interpretation. Semi-Annual Report on China’s Beef Cattle Industry 2025: 99.92 Million Cattle in Inventory Nationwide, 22.03 Million Slaughtered, and 3.42 Million Tons of Beef Produced…[EB/OL]. (2025-07-18) [2025-07-24]. Available at: https://mp.weixin.qq.com/s/PfQYoWvbbvC-o-zsQYnWtA. | |
| [4] | 李俊雅. 肉牛产业发展形势与政策分析[Z]. 石家庄, 2024. |
| LI J Y. Analysis on the Development Situation and Policies of the Beef Cattle Industry[Z]. Shijiazhuang, 2024. | |
| [5] | 中共中央国务院. 关于做好2023年全面推进乡村振兴重点工作的意见[EB/OL]. [2025-06-19]. https://www.moa.gov.cn/ztzl/2023yhwj/2023nzyyhwj/202302/t20230214_6420529.htm. |
| The Central Committee of the Communist Party of China and the State Council. Opinions on Comprehensively Advancing Key Work in Rural Revitalization in 2023[EB/OL]. [2025-06-19]. Available at: https://www.moa.gov.cn/ztzl/2023yhwj/2023nzyyhwj/202302/t20230214_6420529.htm. | |
| [6] | 新华社. 中共中央国务院关于进一步深化农村改革扎实推进乡村全面振兴的意见[EB/OL]. (2025-02-23)[2025-06-19]. https://www.gov.cn/zhengce/202502/content_7005158.htm. |
| Xinhua News Agency. Opinions of the Central Committee of the Communist Party of China and the State Council on Further Deepening Rural Reform and Solidly Advancing Comprehensive Rural Revitalization[EB/OL]. (2025-02-23) [2025-06-19]. Available at: https://www.gov.cn/zhengce/202502/content_7005158.htm. | |
| [7] | Valley Agricultural Software. Welcome to DairyComp 305![EB/OL]. [2025-06-19]. https://dc-help.vas.com/GetStarted/WelcomeToDC305.htm. |
| [8] | 李健, 徐帆, 谢易宸, 等. 基于物联网的肉牛智能养殖系统设计与研究. 吉林农业大学学报, 2023, 45(4): 485-496. |
| LI J, XU F, XIE Y C, et al. Design and research of intelligent beef cattle breeding system based on internet of things. Journal of Jilin Agricultural University, 2023, 45(4): 485-496. | |
| [9] | 张帆, 周梦婷, 唐湘方, 等. 规模化肉牛场数字化管控平台的开发与应用——以阳信亿利源5G数字化牧场为例. 农业大数据学报, 2024, 6(1): 68-81. |
| ZHANG F, ZHOU M T, TANG X F, et al. Development and application of digital control platform in large-scale beef cattle farm — Take the 5G digital ranch of Yangxin Yi Liyuan Halal Meat Co., Ltd. as an example. Journal of Agricultural Big Data, 2024, 6(1): 68-81. | |
| [10] | 金录国, 周奎良. 基于“物联网+人工智能”的肉牛养殖园区技术推广探究. 中国牛业科学, 2024, 50(4): 73-76. |
| JIN L G, ZHOU K L. Exploration of the promotion of beef cattle breeding park technology based on “internet of things + artificial intelligence”. China Cattle Science, 2024, 50(4): 73-76. | |
| [11] | 姜冠旭. 新形势下肉牛养殖在畜牧业发展中的作用分析. 中国动物保健, 2025, 27(3): 157-158. |
| JIANG G X. Analysis of the role of beef cattle breeding in the development of animal husbandry under the new situation. China Animal Health, 2025, 27(3): 157-158. | |
| [12] | 代丽芳. 新质生产力对肉牛规模养殖户绿色生产转型的影响分析. 饲料研究, 2025, 48(3): 195-198. |
| DAI L F. Analysis of impact of new quality productivity on green production transformation of large-scale beef cattle breeders. Feed Research, 2025, 48(3): 195-198. | |
| [13] | 张志亭, 周春凤, 王云军. 我国肉牛养殖模式现状及发展建议. 养殖与饲料, 2024, 23(3): 58-61. |
| ZHANG Z T, ZHOU C F, WANG Y J. Current situation and development suggestions for beef cattle breeding models in China. Breeding and Feed, 2024, 23(3): 58-61. | |
| [14] | 黄桂恒. 中国肉牛养殖行业价值全景概览及投资机会分析. 中国农业信息, 2013(21): 31-32. |
| HUANG G H. Overview of the value landscape and investment opportunities in China’s beef cattle breeding industry. China Agricultural Information, 2013(21): 31-32. | |
| [15] | ARULMOZHI E, DEB N C, TAMRAKAR N, et al. From reality to virtuality: revolutionizing livestock farming through digital twins. Agriculture, 2024, 14(12): 2231. |
| [16] | JO S K, PARK D H, PARK H, et al. Smart livestock farms using digital twin: feasibility study// 2018 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2018: 1461-1463. |
| [17] | JEONG D Y, JO S K, LEE I B, et al. Digital twin application: making a virtual pig house toward digital livestock farming. IEEE Access, 2023, 11: 121592-121602. |
| [18] | AN D, CHEN Y Q. Digital twin enabled methane emission abatement using networked mobile sensing and mobile actuation// 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2021: 354-357. |
| [19] | 于洲, 王浩, 齐仁立, 等. 猪舍智能清洁机器人研究与应用进展. 农业工程学报, 2025, 41(2): 1-11. |
| YU Z, WANG H, QI R L, et al. Research and application progress on intelligent cleaning robots in pigsties. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(2): 1-11. | |
| [20] | SHINIAN H, LIXUE Z H U, HONGNAN H U, et al. Research status and development trends of livestock and poultry cleaning robot. Agricultural Engineering, 2025, 15(4): 1-9. |
| [21] | 尧李慧, 蔡晓华, 田雷, 等. 牛舍清洁机器人结构设计与避障设计. 农机化研究, 2018, 40(2): 70-74, 79. |
| YAO L H, CAI X H, TIAN L, et al. Design and research of automatic barn cleaner structure and obstacle avoidance system. Journal of Agricultural Mechanization Research, 2018, 40(2): 70-74, 79. | |
| [22] | MAZZARI V. ROBOTISED COWSHED CLEANING[EB/OL]. (2021-06-07)[2025-06-24]. https://www.generationrobots.com/blog/en/robotised-cowshed-cleaning. |
| [23] | 杨存志, 贺刚, 尧李慧, 等. 全自走牛舍清洁机器人的设计. 农机化研究, 2017, 39(5): 90-94. |
| YANG C Z, HE G, YAO L H, et al. Design of self-propelled barn cleaning robot. Journal of Agricultural Mechanization Research, 2017, 39(5): 90-94. | |
| [24] | DEGREES 270. Barn cleaner for solid floors | Cleaner Hooves | Lely Discovery Collector[EB/OL]. [2025-06-24]. https://www.lelyna.com/us/solutions/manure/discovery-collector. |
| [25] | MARCHESINI G, MOTTARAN D, CONTIERO B, et al. Use of rumination and activity data as health status and performance indicators in beef cattle during the early fattening period. The Veterinary Journal, 2018, 231: 41-47. |
| [26] | SUN D S, WEBB L, VAN DER TOL P P J, et al. A systematic review of automatic health monitoring in calves: glimpsing the future from current practice. Frontiers in Veterinary Science, 2021, 8: 761468. |
| [27] | O’GRADY M J, LANGTON D, O’HARE G M P. Edge computing: a tractable model for smart agriculture?. Artificial Intelligence in Agriculture, 2019, 3: 42-51. |
| [28] | ?ETINTAV B, AYGüN H B, ESEO?LU H ?, et al. Utilizing physiological metrics and change point analysis for real-time livestock health monitoring. Bal?kes?r Health Sciences Journal, 2025, 14(1): 94-100. |
| [29] | KUMAR A, HANCKE G P. A zigbee-based animal health monitoring system. IEEE Sensors Journal, 2015, 15(1): 610-617. |
| [30] | DOLECHECK K A, SILVIA W J, HEERSCHE G, et al. Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies. Journal of Dairy Science, 2015, 98(12): 8723-8731. |
| [31] | NEVARD R P, PANT S D, BROSTER J C, et al. Maternal behavior in beef cattle: the physiology, assessment and future directions: a review. Veterinary Sciences, 2023, 10(1): 10. |
| [32] | SHARMA B, KOUNDAL D. Cattle health monitoring system using wireless sensor network: a survey from innovation perspective. IET Wireless Sensor Systems, 2018, 8(4): 143-151. |
| [33] | THEURER M E, AMRINE D E, WHITE B J. Remote noninvasive assessment of pain and health status in cattle. Veterinary Clinics of North America: Food Animal Practice, 2013, 29(1): 59-74. |
| [34] | LI D X, LI B S, LI Q, et al. Cattle identification based on multiple feature decision layer fusion. Scientific Reports, 2024, 14(1): 26631. |
| [35] | 彭阳翔, 杨振标, 闫奎友, 等. 从人工到智能:牛个体识别技术研究进展. 中国畜牧兽医, 2023, 50(5): 1855-1866. |
| PENG Y X, YANG Z B, YAN K Y, et al. From artificial to intelligent : research progress of individual identification technology for cattle. China Animal Husbandry & Veterinary Medicine, 2023, 50(5): 1855-1866. | |
| [36] | MAR C C, ZIN T T, TIN P, et al. Cow detection and tracking system utilizing multi-feature tracking algorithm. Scientific Reports, 2023, 13: 17423. |
| [37] | HAO W, REN C, HAN M, et al. Cattle body detection based on YOLOv5-EMA for precision livestock farming. Animals, 2023, 13(22): 3535. |
| [38] | BELLO R W, ZAWAWI A, SUFRIL A, et al. Image-based individual cow recognition using body patterns. International Journal of Advanced Computer Science and Applications, 2020, 11(3):92-98. |
| [39] | WANG Y W, MüCHER S, WANG W S, et al. A review of three-dimensional computer vision used in precision livestock farming for cattle growth management. Computers and Electronics in Agriculture, 2023, 206: 107687. |
| [40] | MILLER G A, HYSLOP J J, ROSS D W, et al. Beef monitor: tracking beef cattle growth and predicting carcass characteristics of live animals[C]. 2018. |
| [41] | COMINOTTE A, FERNANDES A F A, DOREA J R R, et al. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science, 2020, 232: 103904. |
| [42] | VYTELLE SENSE: Monitor and Measure Key Performance Factors[EB/OL]. [2025-06-24]. https://vytelle.com/vytelle-sense. |
| [43] | CHAPINAL N, VEIRA D M, WEARY D M, et al. Technical note: validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of Dairy Science, 2007, 90(12): 5732-5736. |
| [44] | RICHESON J T, LAWRENCE T E, WHITE B J. Using advanced technologies to quantify beef cattle behavior1. Translational Animal Science, 2018, 2(2): 223-229. |
| [45] | smaXtec-System: Feeding[EB/OL]. [2025-06-24]. https://smaxtec.com/us/feeding. |
| [46] | Raytron. Raytron Unveils AI-Powered Thermal Imaging Dual- spectrum Robot to Safeguard Livestock and Industrial Safety-News Center-Raytron[EB/OL]. [2025-09-18]. https://en.raytrontek.com/news/news-detail-958.htmutm_source=chatgpt.com. |
| [47] | VAKULYA G, HAJNAL é, UDVARDY P, et al. In-depth development of a versatile rumen bolus sensor for dairy cattle. Sensors, 2024, 24(21): 6976. |
| [48] | BUSHBY E V, THOMAS M, VáZQUEZ-DIOSDADO J A, et al. Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data. Scientific Reports, 2024, 14: 9737. |
| [49] | CANTOR M C, GOETZ H M, BEATTIE K, et al. Evaluation of an infrared thermography camera for measuring body temperature in dairy calves. JDS Communications, 2022, 3(5): 357-361. |
| [50] | Optimize Feed Management[EB/OL]. [2025-06-24]. https://www.cowmanager.com/cow-management/modules/nutrition. |
| [51] | ZHANG X H, CAO Z Y, DONG W B. Overview of edge computing in the agricultural Internet of Things: key technologies, applications, challenges. IEEE Access, 2020, 8: 141748-141761. |
| [52] | ALONSO R S, SITTóN-CANDANEDO I, GARCíA ó, et al. An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks, 2020, 98: 102047. |
| [53] | CARIA M, SCHUDROWITZ J, JUKAN A, et al. Smart farm computing systems for animal welfare monitoring// 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics. IEEE, 2017: 152-157. |
/
| 〈 |
|
〉 |