肉牛生理指标智能监测技术研究进展与展望

doi:10.12133/j.smartag.SA202312001

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (4): 1-17.doi: 10.12133/j.smartag.SA202312001

• 专题--智慧畜牧技术创新与可持续发展 • 下一篇

肉牛生理指标智能监测技术研究进展与展望

张帆¹, 周梦婷¹, 熊本海¹(), 杨振刚², 刘民泽², 冯文晓³, 唐湘方¹

^1. 中国农业科学院北京畜牧兽医研究所，畜禽营养与饲养全国重点实验室，北京 100193，中国
^2. 阳信亿利源清真肉类有限公司，山东滨州 251800，中国
^3. 北京首农畜牧发展有限公司，北京 102600，中国

收稿日期:2023-12-04 出版日期:2024-07-30
基金项目:
山东省重点研发计划(2022TZXD0013); 国家重点研发计划项目(2023YFD2000701)
作者简介:
张帆，研究方向为反刍动物营养与智慧畜牧业。E-mail： zhangfan07@caas.cn
通信作者:
熊本海，博士，研究员，博士生导师，研究方向为畜禽智能装备。E-mail： xiongbenhai@caas.cn

Research Advances and Prospect of Intelligent Monitoring Systems for the Physiological Indicators of Beef Cattle

ZHANG Fan¹, ZHOU Mengting¹, XIONG Benhai¹(), YANG Zhengang², LIU Minze², FENG Wenxiao³, TANG Xiangfang¹

^1. State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
^2. Yangxin Yiliyuan Halal Meat Co. , Ltd. , Binzhou 251800, China
^3. Beijing Sunlon Livestock Development Co. , Ltd. , Beijing 102600, China

Received:2023-12-04 Online:2024-07-30
Foundation items:Key R&D Program of Shandong Province(2022TZXD0013); National Key R&D Program of China(2023YFD2000701)
About author:
ZHANG Fan, E-mail: zhangfan07@caas.cn
Corresponding author:
XIONG Benhai, E-mail: xiongbenhai@caas.cn

摘要/Abstract

摘要：

[目的/意义] 随着自动化、数智化技术的快速发展及其相关技术在肉牛养殖上的逐步推广利用，肉牛智能化养殖技术研究也取得了一定进步。肉牛的生理指标如运动量、体温、心率、呼吸频率，以及反刍量等变化反映了肉牛的健康或亚健康状态。基于多种传感器采集到的数据以及机器学习、数据挖掘及模型化分析等技术的利用，肉牛的生理指标可由智能感知装备尤其接触式设备自动获取并用于发情、产犊、健康和应激的监测。［进展］针对肉牛养殖过程生理指标的智能监测技术及其利用价值进行了系统分析，分析了生理指标监测技术在实际生产中的应用现状，总结了肉牛生理指标监测的难点和挑战，并提出了未来发展方向。［结论/展望］肉牛生理指标的智能监测与利用既提高数据采集的时效性和准确性，有利于提高一线人员工作效率，促进肉牛养殖的智能化水平及健康养殖水平。结合当前中国肉牛实际饲养现状和肉牛生理指标智能监测传感器的研究现状，未来需降低接触类相关设备能耗、提高使用寿命；提高各监测数据的相互融合深度分析，提高监测准确率；加强非接触、高精度、自动化的数据采集分析技术研发，减少人工佩戴设备的工作量和设备使用成本。

关键词: 肉牛生理指标, 人工智能, 智能监测, 传感器, 数据融合

Abstract:

[Significance] The beef cattle industry plays a pivotal role in the development of China's agricultural economy and the enhancement of people's dietary structure. However, there exists a substantial disparity in feeding management practices and economic efficiency of beef cattle industry compared to developed countries. While the beef cattle industry in China is progressing towards intensive, modern, and large-scale development, it encounters challenges such as labor shortage and rising labor costs that seriously affect its healthy development. The determination of animal physiological indicators plays an important role in monitoring animal welfare and health status. Therefore, leveraging data collected from various sensors as well as technologies like machine learning, data mining, and modeling analysis enables automatic acquisition of meaningful information on beef cattle physiological indicators for intelligent management of beef cattle. In this paper, the intelligent monitoring technology of physiological indicators in beef cattle breeding process and its application value are systematically summarized, and the existing challenges and future prospects of intelligent beef cattle breeding process in China are prospected. [Progress] The methods of obtaining information on beef cattle physiological indicators include contact sensors worn on the body and non-contact sensors based on various image acquisitions. Monitoring the exercise behavior of beef cattle plays a crucial role in disease prevention, reproduction monitoring, and status assessment. The three-axis accelerometer sensor, which tracks the amount of time that beef cattle spend on lying, walking, and standing, is a widely used technique for tracking the movement behavior of beef cattle. Through machine vision analysis, individual recognition of beef cattle and identification of standing, lying down, and straddling movements can also be achieved, with the characteristics of non-contact, stress-free, low cost, and generating high data volume. Body temperature in beef cattle is associated with estrus, calving, and overall health. Sensors for monitoring body temperature include rumen temperature sensors and rectal temperature sensors, but there are issues with their inconvenience. Infrared temperature measurement technology can be utilized to detect beef cattle with abnormal temperatures by monitoring eye and ear root temperatures, although the accuracy of the results may be influenced by environmental temperature and monitoring distance, necessitating calibration. Heart rate and respiratory rate in beef cattle are linked to animal diseases, stress, and pest attacks. Monitoring heart rate can be accomplished through photoelectric volume pulse wave measurement and monitoring changes in arterial blood flow using infrared emitters and receivers. Respiratory rate monitoring can be achieved by identifying different nostril temperatures during inhalation and exhalation using thermal infrared imaging technology. The ruminating behavior of beef cattle is associated with health and feed nutrition. Currently, the primary tools used to detect rumination behavior are pressure sensors and three-axis accelerometer sensors positioned at various head positions. Rumen acidosis is a major disease in the rapid fattening process of beef cattle, however, due to limitations in battery life and electrode usage, real-time pH monitoring sensors placed in the rumen are still not widely utilized. Changes in animal physiology, growth, and health can result in alterations in specific components within body fluids. Therefore, monitoring body fluids or surrounding gases through biosensors can be employed to monitor the physiological status of beef cattle. By processing and analyzing the physiological information of beef cattle, indicators such as estrus, calving, feeding, drinking, health conditions, and stress levels can be monitored. This will contribute to the intelligent development of the beef cattle industry and enhance management efficiency. While there has been some progress made in developing technology for monitoring physiological indicators of beef cattle, there are still some challenges that need to be addressed. Contact sensors consume more energy which affects their lifespan. Various sensors are susceptible to environmental interference which affects measurement accuracy. Additionally, due to a wide variety of beef cattle breeds, it is difficult to establish a model database for monitoring physiological indicators under different feeding conditions, breeding stages, and breeds. Furthermore, the installation cost of various intelligent monitoring devices is relatively high, which also limits its utilization coverage. [Conclusion and Prospects] The application of intelligent monitoring technology for beef cattle physiological indicators is highly significance in enhancing the management level of beef cattle feeding. Intelligent monitoring systems and devices are utilized to acquire physiological behavior data, which are then analyzed using corresponding data models or classified through deep learning techniques to promptly monitor subtle changes in physiological indicators. This enables timely detection of sick, estrus, and calving cattle, facilitating prompt measures by production managers, reducing personnel workload, and improving efficiency. The future development of physiological indicators monitoring technologies in beef cattle primarily focuses on the following three aspects: (1) Enhancing the lifespan of contact sensors by reducing energy consumption, decreasing data transmission frequency, and improving battery life. (2) Integrating and analyzing various monitoring data from multiple perspectives to enhance the accuracy and utility value. (3) Strengthening research on non-contact, high-precision and automated analysis technologies to promote the precise and intelligent development within the beef cattle industry.

Key words: beef cattle physiological indicator, artificial intelligence, intelligent monitoring, sensor, data fusion

张帆, 周梦婷, 熊本海, 杨振刚, 刘民泽, 冯文晓, 唐湘方. 肉牛生理指标智能监测技术研究进展与展望[J]. 智慧农业(中英文), 2024, 6(4): 1-17.

ZHANG Fan, ZHOU Mengting, XIONG Benhai, YANG Zhengang, LIU Minze, FENG Wenxiao, TANG Xiangfang. Research Advances and Prospect of Intelligent Monitoring Systems for the Physiological Indicators of Beef Cattle[J]. Smart Agriculture, 2024, 6(4): 1-17.

图/表 8

图1

表1

肉牛生理参数监测传感器的功能与应用研究情况

类别	传感器	生产商或性能	监测部位	算法	作用	精度或结果	共性问题	参考文献
接触式传感器	加速度传感器+GPS	Digitanimal	颈部	RF算法识别行为、K-medoids无监督机器学习算法识别位置	躺卧、站立、吃草及反刍行为识别	93%	1. 电池续航力短，影响使用寿命 2. 设备成本高，佩戴任务重，影响使用覆盖率 3. 使用时需结合人工诊断	Cabezas等^［28］
	温度传感器	Thermobolus	瘤胃	—	呼吸道疾病诊断	提前12~6 h发现		Timsit等^［29］
	温度传感器	Medria Vel'Phone^®	阴道	—	分娩预测	产前24 h为89%		Ricci等^［30］
	加速度传感器	Axivity	尾根	RF模型	分娩预测	产前2 h为5.4%		Miller等^［31］
	压力传感器	0~2 000.000 g	嘴部	FFT提取特征值，XGB算法识别反刍	反刍监测	96.6%		Chen等^［32］
	声音传感器	Sennheiser ME 2-US	嘴部	SIGNAL软件	采食量预测	95%识别咬和咀嚼		Clapham等^［33］
	瘤胃pH计	参比电极加阀门	瘤胃	—	瘤胃pH监测	误差0.07，寿命增加到2年		Higuchi等^［34］
	超声波	MyLabOne™	瘤胃壁厚度	—	通过瘤胃壁检测瘤胃酸中毒	瘤胃壁与pH显著相关		Fiore等^［35］
非接触式传感器	彩色摄像机	海康威视DS-IPC-T12H-Ia	整体图像	DB-TEAF方法	异常行为识别	92.16%	1. 易受环境干扰 2. 品种多，建模任务较大 3. 数据量大，设备质量要求高，前期投入成本高	Han等^［36］
	彩色摄像机	IMS系统	整体图像	YOLO v5增强识别模型	结合爬跨行为与活动量识别发情	可识别相似牛个体与爬跨行为		Cho和Kim^［7］
	ZED摄像机	分辨率：1 920× 1 080，帧率：30 f/s	侧后方	CNN和LSTM	个体识别	15 f/s时88%， 20 f/s时91%		Qiao等^［37］
	红外摄像机	Testo 890	眼球、耳根	IRSoft 4.0	体温测定	眼球更接近体温，耳根受环境干扰		Giro等^［38］
	红外摄像机	FLIR One Pro	鼻孔	目标检测和实例分割Mask R-CNN	呼吸频率	76%		Kim和 Hidaka^［39］

表1

图2

图3

图4

图5

图6

图7

参考文献 101

1	王明利. 2022年肉牛产业发展形势及2023年展望[J]. 中国畜禽种业, 2023, 19(4): 41-45.
	WANG M L. Development situation of beef cattle industry in 2022 and prospect in 2023[J]. The Chinese livestock and poultry breeding, 2023, 19(4): 41-45.
2	2022~2023年畜牧行业观察第三章中国畜牧业发展现状分析[M/OL]// 畜禽养殖与饲料产业年鉴, 2023, 30-34, 60. [2023-11-10].
3	冉桂霞, 李双, 田天, 等. 高档肉牛生产现状及发展方向[J]. 中国畜牧业, 2022(23): 34-35.
	RAN G X, LI S, TIAN T, et al. Present situation and development direction of high-grade beef cattle production[J]. China animal industry, 2022(23): 34-35.
4	ZHU W B, CHEN Y F, ZHAO J, et al. Impacts of household income on beef at-home consumption: Evidence from urban China[J]. Journal of integrative agriculture, 2021, 20(6): 1701-1715.
5	FREITAG G P, DE LIMA L G F, JACOMINI J A, et al. An accurate image analysis method for estimating body measurements in horses[J]. Journal of equine veterinary science, 2021, 101: ID 103418.
6	LI J W, MA W H, BAI Q, et al. A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data[J]. Biosystems engineering, 2023, 230:171-190.
7	CHO Y, KIM J. AI-based intelligent monitoring system for estrus prediction in the livestock industry[J]. Applied sciences, 2023, 13(4): ID 2442.
8	沈楷祖, 滚双宝. 肉牛生长发育规律及常规生理指标研究综述[J]. 甘肃畜牧兽医, 2017, 47(9): 102-103.
	SHEN K Z, GUN S B. Review on growth and development law and routine physiological indexes of beef cattle[J]. Gansu animal husbandry and veterinary, 2017, 47(9): 102-103.
9	杨亮, 熊本海, 王辉, 等. 家畜饲喂机器人研究进展与发展展望[J]. 智慧农业(中英文), 2022(2): 86-98.
	YANG L, XIONG B H, WANG H, et al. Research progress and outlook of livestock feeding robot[J]. Smart agriculture, 2022(2): 86-98.
10	KENDRICK K M. Intelligent perception[J]. Applied animal behaviour science, 1998, 57(3/4): 213-231.
11	KING A. Technology: The future of agriculture[J]. Nature, 2017, 544: S21-S23.
12	ELLIS J L, ELLIS J L, JACOBS M, et al. Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data[J]. Animal, 2020, 14(s2): s223-s237.
13	赵树林, 盖凌云, 孙晓婷, 等. 肉牛产业链智能感知技术研究与应用系统设计[J]. 青岛农业大学学报(自然科学版), 2023, 40(4): 300-303.
	ZHAO S L, GAI L Y, SUN X T, et al. Research on and application system design of intelligent sensing technology in beef cattle industry[J]. Journal of Qingdao agricultural university (natural science), 2023, 40(4): 300-303.
14	CHENG M, YUAN H B, WANG Q F, et al. Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect[J]. Computers and electronics in agriculture, 2022, 198: ID 107010.
15	李奇峰, 王文婷, 余礼根, 等. 信息技术在畜禽养殖中的应用进展[J]. 中国农业信息, 2018, 30(2): 15-23, 41.
	LI Q F, WANG W T, YU L G, et al. Research review of information technologies for livestock[J]. China agricultural informatics, 2018, 30(2): 15-23, 41.
16	QIAO Y L, KONG H, CLARK C, et al. Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation[J]. Computers and electronics in agriculture, 2021, 185: ID 106143.
17	ROSELL-POLO J R, AUAT CHEEIN F, GREGORIO E, et al. Advances in structured light sensors applications in precision agriculture and livestock farming[J]. Advances in agronomy, 2015, 133: 71-112.
18	WANG J, ZHANG Y D, WANG J P, et al. Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag[J]. Biosystems engineering, 2022, 214: 193-206.
19	VAN LEERDAM M, HUT P R, LISEUNE A, et al. A predictive model for hypocalcaemia in dairy cows utilizing behavioural sensor data combined with deep learning[J]. Computers and electronics in agriculture, 2024, 220: ID 108877.
20	谢忠红, 刘悦怡, 宋子阳, 等. 基于时序运动特征的奶牛爬跨行为识别研究[J]. 南京农业大学学报, 2021, 44(1): 194-200.
	XIE Z H, LIU Y Y, SONG Z Y, et al. Research on recognition of crawling behavior of cows based on temporal motion features[J]. Journal of Nanjing agricultural university, 2021, 44(1): 194-200.
21	STRUTZKE S, FISKE D, HOFFMANN G, et al. Technical note: Development of a noninvasive respiration rate sensor for cattle[J]. Journal of dairy science, 2019, 102(1): 690-695.
22	刘志永. 肉牛的饲养管理要点及繁育技术[J]. 畜禽业, 2023, 34(9): 27-29, 33.
	LIU Z Y. Key points of feeding management and breeding technology of beef cattle[J]. Livestock and poultry industry, 2023, 34(9): 27-29, 33.
23	帕力旦木·阿布都外力. 奶牛不同阶段饲养管理技术要点[J]. 中国畜禽种业, 2022, 18(6): 111-112.
	PALIDANMU A. Key points of feeding management technology of dairy cows at different stages[J]. The Chinese livestock and poultry breeding, 2022, 18(6): 111-112.
24	梁晨宏. 肉牛与奶牛早期天然免疫发育基因的研究与开发[D]. 呼和浩特: 内蒙古农业大学, 2022.
	LIANG C H. Research and development of early innate immunity developmental gene in beef and dairy cattle[D]. Hohhot: Inner Mongolia Agricultural University, 2022.
25	WANG J, ZHANG H Y, JI J T, et al. Development of a wireless measurement system for classifying cow behavior using accelerometer data and location data[J]. Applied engineering in agriculture, 2019, 35(2): 135-147.
26	LI Z Y, SONG L, DUAN Y C, et al. Basic motion behaviour recognition of dairy cows based on skeleton and hybrid convolution algorithms[J]. Computers and electronics in agriculture, 2022, 196: ID 106889.
27	CHEN X M, OGDAHL W, HULSMAN HANNA L L, et al. Evaluation of beef cattle temperament by eye temperature using infrared thermography technology[J]. Computers and electronics in agriculture, 2021, 188: ID 106321.
28	CABEZAS J, YUBERO R, VISITACIÓN B, et al. Analysis of accelerometer and GPS data for cattle behaviour identification and anomalous events detection[J]. Entropy (basel), 2022, 24(3): ID 336.
29	TIMSIT E, ASSIÉ S, QUINIOU R, et al. Early detection of bovine respiratory disease in young bulls using reticulo-rumen temperature boluses[J]. The veterinary journal, 2011, 190(1): 136-142.
30	RICCI A, RACIOPPI V, IOTTI B, et al. Assessment of the temperature cut-off point by a commercial intravaginal device to predict parturition in Piedmontese beef cows[J]. Theriogenology, 2018, 113: 27-33.
31	MILLER G A, MITCHELL M, BARKER Z E, et al. Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows[J]. Animal, 2020, 14(6): 1304-1312.
32	CHEN G P, LI C, GUO Y, et al. Recognition of cattle's feeding behaviors using noseband pressure sensor with machine learning[J]. Frontiers in veterinary science, 2022, 9: ID 822621.
33	CLAPHAM W M, FEDDERS J M, BEEMAN K, et al. Acoustic monitoring system to quantify ingestive behavior of free-grazing cattle[J]. Computers and electronics in agriculture, 2011, 76(1): 96-104.
34	HIGUCHI S, OKADA H, TAKAMATSU S, et al. Valve-actuator-integrated reference electrode for an ultra-long-life rumen pH sensor[J]. Sensors (basel), 2020, 20(5): ID E1249.
35	FIORE E, FAILLACE V, MORGANTE M, et al. A retrospective study on transabdominal ultrasound measurements of the rumen wall thickness to evaluate chronic rumen acidosis in beef cattle[J]. BMC veterinary research, 2020, 16(1): ID 337.
36	HAN Y M, WU J, ZHANG H M, et al. Beef cattle abnormal behaviour recognition based on dual-branch frequency channel temporal excitation and aggregation[J]. Biosystems engineering, 2024, 241: 28-42.
37	QIAO Y L, SU D, KONG H, et al. Individual cattle identification using a deep learning based framework[J]. IFAC-Papers on line, 2019, 52(30): 318-323.
38	GIRO A, BERNARDI A C C, BARIONI JUNIOR W, et al. Application of microchip and infrared thermography for monitoring body temperature of beef cattle kept on pasture[J]. Journal of thermal biology, 2019, 84: 121-128.
39	KIM S, HIDAKA Y. Breathing pattern analysis in cattle using infrared thermography and computer vision[J]. Animals (basel), 2021, 11(1): 207.
40	RIABOFF L, SHALLOO L, SMEATON A F, et al. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data[J]. Computers and electronics in agriculture, 2022, 192: ID 106610.
41	CHARLTON G, GAULD C, VERONESI F, et al. Assessing the accuracy of leg mounted sensors for recording dairy cow behavioural activity at pasture, in cubicle housing and a straw yard[J]. Animals (basel), 2022, 12(5): ID 638.
42	ALIPIO M, VILLENA M L. Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification[J]. Smart health, 2023, 27: ID 100369.
43	高学杰. 高产奶牛养殖技术要点与疾病防控[J]. 畜禽业, 2022, 33(3): 128-130.
	GAO X J. Key points of breeding technology and disease prevention and control of high-yield dairy cows[J]. Livestock and poultry industry, 2022, 33(3): 128-130.
44	KAHLON G S, SINGH H, SAINI M, et al. An intelligent framework to detect and generate alert while cattle lying on road in dangerous states using surveillance videos[J]. Multimedia tools and applications, 2023, 82(22): 34589-34607.
45	SCHMIDT M, HOFFMANN G, AMMON C, et al. Application of infrared thermography on lactating sows[J]. Landtechnik, 2013, 68(4): 228-231.
46	HOFFMANN G, SCHMIDT M, AMMON C, et al. Monitoring the body temperature of cows and calves using video recordings from an infrared thermography camera[J]. Veterinary research communications, 2013, 37(2): 91-99.
47	陈静, 蒋安, 孙晓燕, 等. 红外线测温仪在牛体温测定中的效果观察[J]. 草学, 2018(3): 69-71.
	CHEN J, JIANG A, SUN X Y, et al. Observation on the effect of infrared thermometer in bovine body temperature measurement[J]. Journal of grassland and forage science, 2018(3): 69-71.
48	宣小龙, 吴潇, 朱红娟, 等. 牛耳温度与直肠温度的相关性比较试验[J]. 贵州畜牧兽医, 2021, 45(1): 22-24.
	XUAN X L, WU X, ZHU H J, et al. Correlation test of cow's ear temperature and rectal temperature[J]. Guizhou journal of animal husbandry & veterinary medicine, 2021, 45(1): 22-24.
49	CHUNG H, LI J J, KIM Y, et al. Using implantable biosensors and wearable scanners to monitor dairy cattle's core body temperature in real-time[J]. Computers and electronics in agriculture, 2020, 174: ID 105453.
50	孙秀玉, 王之保, 李静, 等. 夏南牛体温、心跳、呼吸生理指标测量试验研究(二报)[J]. 中国牛业科学, 2018, 44(5): 36-38.
	SUN X Y, WANG Z B, LI J, et al. Experimental study on measurement of body temperature, heartbeat and respiratory physiological index of Xia'nan cattle[J]. China cattle science, 2018, 44(5): 36-38.
51	柏中林, 李静, 孙秀玉, 等. 夏南牛体温、心跳、呼吸生理指标测量试验研究[J]. 中国牛业科学, 2016, 42(5): 11-13, 22.
	BAI Z L, LI J, SUN X Y, et al. Study on the physiological indexes body temperature, heart rate and breathing in Xia'nan cattle[J]. China cattle science, 2016, 42(5): 11-13, 22.
52	侯引绪, 张凡建, 魏朝利, 等. 热应激对泌乳牛呼吸频率、心率及体温的影响[J]. 中国奶牛, 2012(7): 52-53.
	HOU Y X, ZHANG F J, WEI Z L, et al. Effects of heat stress on respiratory frequency, heart rate and body temperature of lactating cows[J]. China dairy cattle, 2012(7): 52-53.
53	JORQUERA-CHAVEZ M, FUENTES S, DUNSHEA F R, et al. Modelling and validation of computer vision techniques to assess heart rate, eye temperature, ear-base temperature and respiration rate in cattle[J]. Animals (basel), 2019, 9(12): E1089.
54	ARSHAD J, AHMAD SIDDIQUI T, SHEIKH M I, et al. Deployment of an intelligent and secure cattle health monitoring system[J]. Egyptian informatics journal, 2023, 24(2): 265-275.
55	LOWE G, SUTHERLAND M, WAAS J, et al. Infrared thermography-a non-invasive method of measuring respiration rate in calves[J]. Animals (basel), 2019, 9(8): E535.
56	姬江涛, 刘启航, 高荣华, 等. 基于改进FlowNet 2.0光流算法的奶牛反刍行为分析方法[J]. 农业机械学报, 2023, 54(1): 235-242.
	JI J T, LIU Q H, GAO R H, et al. Ruminant behavior analysis method of dairy cows with improved FlowNet 2.0 optical flow algorithm[J]. Transactions of the Chinese society for agricultural machinery, 2023, 54(1): 235-242.
57	BRAUN U, TRÖSCH L, NYDEGGER F, et al. Evaluation of eating and rumination behaviour in cows using a noseband pressure sensor[J]. BMC veterinary research, 2013, 9(1): ID 164.
58	BIKKER J P, VAN LAAR H, RUMP P, et al. Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity[J]. Journal of dairy science, 2014, 97(5): 2974-2979.
59	BORCHERS M R, CHANG Y M, PROUDFOOT K L, et al. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle[J]. Journal of dairy science, 2017, 100(7): 5664-5674.
60	王月明, 陈甜甜. 基于计算机视觉的牛反刍行为识别与分析[J]. 中国畜牧杂志, 2022, 58(9): 203-208.
	WANG Y M, CHEN T T. Recognition and analysis of cattle ruminating behavior based on computer vision[J]. Chinese journal of animal science, 2022, 58(9): 203-208.
61	卜也, 孟祥人, 刘利, 等. 肉牛生产中瘤胃酸中毒的诊断与治疗[J]. 黑龙江八一农垦大学学报, 2023, 35(4): 51-53.
	BU Y, MENG X R, LIU L, et al. Diagnosis and treatment of rumen acidosis in beef production[J]. Journal of Heilongjiang bayi agricultural university, 2023, 35(4): 51-53.
62	O'GRADY L, DOHERTY M L, MULLIGAN F J. Subacute ruminal acidosis (SARA) in grazing Irish dairy cows[J]. The veterinary journal, 2008, 176(1): 44-49.
63	MORGANTE M, STELLETTA C, BERZAGHI P, et al. Subacute rumen acidosis in lactating cows: An investigation in intensive Italian dairy herds[J]. Journal of animal physiology and animal nutrition, 2007, 91(5/6): 226-234.
64	PENNER G B, BEAUCHEMIN K A, MUTSVANGWA T. An evaluation of the accuracy and precision of a stand-alone submersible continuous ruminal pH measurement system[J]. Journal of dairy science, 2006, 89(6): 2132-2140.
65	SATO S. Pathophysiological evaluation of subacute ruminal acidosis (SARA) by continuous ruminal pH monitoring[J]. Animal science journal, 2016, 87(2): 168-177.
66	BHALLA N, JOLLY P, FORMISANO N, et al. Introduction to biosensors[J]. Essays in biochemistry, 2016, 60(1): 1-8.
67	GRIESCHE C, BAEUMNER A J. Biosensors to support sustainable agriculture and food safety[J]. TrAC trends in analytical chemistry, 2020, 128: ID 115906.
68	REID S M, FERRIS N P, BRÜNING A, et al. Development of a rapid chromatographic strip test for the pen-side detection of foot-and-mouth disease virus antigen[J]. Journal of virological methods, 2001, 96(2): 189-202.
69	CARRARA S, BOLOMEY L, BOERO C, et al. Remote system for monitoring animal models with single-metabolite bio-nano-sensors[J]. IEEE sensors journal, 2013, 13(3): 1018-1024.
70	周正义. 肉牛发情期尾部活动量变化规律与预同期处理妊娠率分析[D]. 太谷: 山西农业大学, 2021.
	ZHOU Z Y. Analysis on the variation of tail activity during estrus and the pregnancy rate of pre-synchronization treatment in beef cattle[D].Taigu: Shanxi Agricultural University, 2021.
71	宣小龙. 体温智能监测系统对肉牛发情配种和疾病诊断的效果及效益分析[J]. 贵州畜牧兽医, 2021, 45(4): 48-49.
	XUAN X L. The effect and benefit analysis of intelligent body temperature monitoring aystem on beef cattle estrus breeding and disease diagnosis[J]. Guizhou journal of animal husbandry & veterinary medicine, 2021, 45(4): 48-49.
72	DOLECHECK K A, SILVIA W J, HEERSCHE G, et al. Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies[J]. Journal of dairy science, 2015, 98(12): 8723-8731.
73	NELSON S T, HAADEM C S, NØDTVEDT A, et al. Automated activity monitoring and visual observation of estrus in a herd of loose housed Hereford cattle: Diagnostic accuracy and time to ovulation[J]. Theriogenology, 2017, 87: 205-211.
74	BARRIER A C, HASKELL M J. Calving difficulty in dairy cows has a longer effect on saleable milk yield than on estimated milk production[J]. Journal of dairy science, 2011, 94(4): 1804-1812.
75	NAGEL C, AURICH J, AURICH C. Prediction of the onset of parturition in horses and cattle[J]. Theriogenology, 2020, 150: 308-312.
76	CHANG A Z, FOGARTY E S, SWAIN D L, et al. Accelerometer derived rumination monitoring detects changes in behaviour around parturition[J]. Applied animal behaviour science, 2022, 247: ID 105566.
77	OLIVEIRA B R, RIBAS M N, MACHADO F S, et al. Validation of a system for monitoring individual feeding and drinking behaviour and intake in young cattle[J]. Animal, 2018, 12(3):634-639.
78	ANDRIAMANDROSO A L H, BINDELLE J, MERCATORIS B, et al. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing[J]. Biotechnologie agronomie societe et environnement, 2016: 273-286.
79	周雅婷. 肉牛采食行为识别与采食量模型研究[D]. 沈阳: 沈阳农业大学, 2018.
	ZHOU Y T. Study on the identification of eating behavior of beef cattle and the model of feed intake[D].Shenyang: Shenyang Agricultural University, 2018.
80	姜美曦, 陈春玲, 周云成. 基于卡尔曼滤波器的肉牛行为识别方法[J]. 江苏农业科学, 2017, 45(12): 167-171.
	JIANG M X, CHEN C L, ZHOU Y C. Behavior identification method of beef cattle based on Kalman filter[J]. Jiangsu agricultural sciences, 2017, 45(12): 167-171.
81	SCHWARTZKOPF-GENSWEIN K S, HUISMA C, MCALLISTER T A. Validation of a radio frequency identification system for monitoring the feeding patterns of feedlot cattle[J]. Livestock production science, 1999, 60(1): 27-31.
82	RUTTER S M. Graze: A program to analyze recordings of the jaw movements of ruminants[J]. Behavior research methods, instruments, & computers, 2000, 32(1): 86-92.
83	GALLI J R, CANGIANO C A, PECE M A, et al. Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle[J]. Animal, 2018, 12(5): 973-982.
84	WILLIAMS L R, MOORE S T, BISHOP-HURLEY G J, et al. A sensor-based solution to monitor grazing cattle drinking behaviour and water intake[J]. Computers and electronics in agriculture, 2020, 168: ID 105141.
85	TAHSIN K. Development of a propeller P8X 32A based wireless biosensor system for cattle health monitoring and disease detection[J]. British journal of applied science & technology, 2016, 18(2): 1-14.
86	HELWATKAR A, RIORDAN D, WALSH J. Sensor technology for animal health monitoring[J]. International journal on smart sensing and intelligent systems, 2014, 7(5): 1-6.
87	房昊源, 杨亮, 王洪壮, 等. 夏季横向交互送风系统对肉牛生理和生产性能的影响[J]. 中国农业科学, 2022, 55(5): 1025-1036.
	FANG H Y, YANG L, WANG H Z, et al. Effects of cross-ventilation system on physiology and production performance of beef cattle in summer[J]. Scientia agricultura sinica, 2022, 55(5): 1025-1036.
88	杨梓曼, 尚相龙, 陈豪, 等. 热应激对肉牛血清生化指标、瘤胃发酵参数及微生物区系的影响[J]. 动物营养学报, 2022, 34(7): 4487-4497.
	YANG Z M, SHANG X L, CHEN H, et al. Effects of heat stress on serum biochemical indexes, rumen fermentation parameters and microflora of beef cattle[J]. Chinese journal of animal nutrition, 2022, 34(7): 4487-4497.
89	BROWN-BRANDL T M, EIGENBERG R A, NIENABER J A, et al. Dynamic response indicators of heat stress in shaded and non-shaded feedlot cattle, part 1: Analyses of indicators[J]. Biosystems engineering, 2005, 90(4): 451-462.
90	李璟辉, 程琼仪, 陈昭晖. 利用呼吸频率比较不同温热指数在牛舍的应用[J]. 家畜生态学报, 2014, 35(7): 32-36.
	LI J H, CHENG Q Y, CHEN Z H. Comparing different thermal indices with respiration rate application and its in beef cattle barn[J]. Journal of domestic animal ecology, 2014, 35(7): 32-36.
91	CHAPMAN N H, CHLINGARYAN A, THOMSON P C, et al. A deep learning model to forecast cattle heat stress[J]. Computers and electronics in agriculture, 2023, 211: ID 107932.
92	DE SOUSA R V, CANATA T F, LEME P R, et al. Development and evaluation of a fuzzy logic classifier for assessing beef cattle thermal stress using weather and physiological variables[J]. Computers and electronics in agriculture, 2016, 127: 176-183.
93	陈丽媛, 洪小华, 颜培实. 中国南方冬季和夏季肉牛体感温度研究[J]. 畜牧与兽医, 2015, 47(2): 40-44.
	CHEN L Y, HONG X H, YAN P S. Effective temperature equation of cows during winter and summer seasons in Southern of China[J]. Animal husbandry & veterinary medicine, 2015, 47(2): 40-44.
94	CUTHBERTSON H, TARR G, LOUDON K, et al. Using infrared thermography on farm of origin to predict meat quality and physiological response in cattle (Bos Taurus) exposed to transport and marketing[J]. Meat science, 2020, 169: ID 108173.
95	LIU L S, NI J Q, ZHAO R Q, et al. Design and test of a low-power acceleration sensor with bluetooth low energy on ear tags for sow behaviour monitoring[J]. Biosystems engineering, 2018, 176: 162-171.
96	王军波, 杨云, 陈康林, 等. 规模化肉牛养殖场的设计方案[J]. 贵州畜牧兽医, 2023, 47(6): 43-45.
	WANG J B, YANG Y, CHEN K L, et al. Design scheme for large-scale beef cattle breeding farms[J]. Guizhou journal of animal husbandry & veterinary medicine, 2023, 47(6): 43-45.
97	李威风. 基于LoRa的肉牛智能测温耳标设计及系统搭建[D]. 合肥: 安徽大学, 2023.
	LI W F. Design and system construction of intelligent temperature measurement earmark for beef cattle based on LoRa[D]. Hefei: Anhui University, 2023.
98	张晗, 吕思潼, 杨文静, 等. 畜牧场蚊蝇防控方法及应用研究进展[J]. 家畜生态学报, 2022, 43(5): 81-85.
	ZHANG H, LYU S T, YANG W J, et al. Advances in prevention and control methods of mosquitoes and flies in livestock farms[J]. Journal of domestic animal ecology, 2022, 43(5): 81-85.
99	张焕. 肉牛皮蝇蛆病的流行病学、临床症状、诊断及防治[J]. 现代畜牧科技, 2021(5): 157-158.
	ZHANG H. Epidemiology, clinical symptoms, diagnosis, prevention and treatment of myiasis in beef hide[J]. Modern animal husbandry science & technology, 2021(5): 157-158.
100	MENG Y, YOON S, HAN S, et al. Improving known-unknown cattle's face recognition for smart livestock farm management[J]. Animals (basel), 2023, 13(22): ID 3588.
101	唐瑜嵘,沈明霞,薛鸿翔,等. 人工智能技术在畜禽养殖业的发展现状与展望[J]. 智能化农业装备学报(中英文), 2023, 4 (1): 1-16.
	TANG Y R, SHEN M X, XUE H X, et al. Development status and prospect of artificial intelligence technology in livestock and poultry breeding[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 1-16.

[1]	刘洋, 冀杰, 潘登, 赵立军, 李明生. 基于激光雷达与IMU融合的农业机器人定位方法[J]. 智慧农业(中英文), 2024, 6(3): 94-106.
[2]	郭旺, 杨雨森, 吴华瑞, 朱华吉, 缪祎晟, 顾静秋. 农业大模型：关键技术、应用分析与发展方向[J]. 智慧农业(中英文), 2024, 6(2): 1-13.
[3]	李露, 葛玉卿, 赵建龙. 基于二硫化钼的电容式土壤湿度传感器[J]. 智慧农业(中英文), 2024, 6(1): 28-35.
[4]	卫倩, 高原源, 李爱学. 油菜素内酯原位检测电化学免疫传感器[J]. 智慧农业(中英文), 2024, 6(1): 76-88.
[5]	王儒敬. 农业传感器：研究进展、挑战与展望[J]. 智慧农业(中英文), 2024, 6(1): 1-17.
[6]	陈睿韵, 田文斌, 鲍海波, 李端, 谢鑫浩, 郑永军, 谭彧. 农业轮式机器人三维环境感知技术研究进展[J]. 智慧农业(中英文), 2023, 5(4): 16-32.
[7]	毛克彪, 张晨阳, 施建成, 王旭明, 郭中华, 李春树, 董立新, 吴门新, 孙瑞静, 武胜利, 姬大彬, 蒋玲梅, 赵天杰, 邱玉宝, 杜永明, 徐同仁. 基于人工智能的地球物理参数反演范式理论及判定条件[J]. 智慧农业(中英文), 2023, 5(2): 161-171.
[8]	刘又夫, 肖德琴, 周家鑫, 卞智逸, 招胜秋, 黄一桂, 王文策. 水禽智能化养殖研究现状及发展趋势[J]. 智慧农业(中英文), 2023, 5(1): 99-110.
[9]	桂泽春, 赵思健. 人工智能在农业风险管理中的应用研究综述[J]. 智慧农业(中英文), 2023, 5(1): 82-98.
[10]	卓越, 丁峰, 严海军, 徐婧. 无人机遥感在饲草作物生长监测中的应用研究进展[J]. 智慧农业(中英文), 2022, 4(4): 35-48.
[11]	赵瑞雪, 杨晨雪, 郑建华, 李娇, 王剑. 农业智能知识服务研究现状及展望[J]. 智慧农业(中英文), 2022, 4(4): 105-125.
[12]	王辉, 陈睿鹏, 余志雪, 贺越, 张帆, 熊本海. 基于卟啉和半导体单壁碳纳米管的场效应气体传感器检测草莓恶疫霉[J]. 智慧农业(中英文), 2022, 4(3): 143-151.
[13]	杨亮, 熊本海, 王辉, 陈睿鹏, 赵一广. 家畜饲喂机器人研究进展与发展展望[J]. 智慧农业(中英文), 2022, 4(2): 86-98.
[14]	耿闻轩, 赵俊晔, 阮继伟, 侯跃辉. 人工智能辅助种植策略对温室草莓生产调控效果对比研究[J]. 智慧农业(中英文), 2022, 4(2): 183-193.
[15]	董宏图, 周思蒙, 王清涛, 王成, 罗斌, 李爱学. 基于羧基化石墨烯-海藻酸钠复合材料的脱落酸电化学免疫传感器的构建及应用[J]. 智慧农业(中英文), 2022, 4(1): 110-120.

肉牛生理指标智能监测技术研究进展与展望

Research Advances and Prospect of Intelligent Monitoring Systems for the Physiological Indicators of Beef Cattle

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 101

相关文章 15

编辑推荐

Metrics

本文评价