奶牛运动行为智能监测研究进展与技术趋势

doi:10.12133/j.smartag.SA202203011

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 36-52.doi: 10.12133/j.smartag.SA202203011

• 专题——智慧畜牧关键技术与装备 • 上一篇下一篇

奶牛运动行为智能监测研究进展与技术趋势

王政¹^,²^,³(), 宋怀波¹^,²^,³(), 王云飞¹^,²^,³, 华志新¹^,²^,³, 李嵘¹^,²^,³, 许兴时¹^,²^,³

^1.西北农林科技大学机械与电子工程学院，陕西杨凌 712100
^2.农业农村部农业物联网重点实验室，陕西杨凌 712100
^3.陕西省农业信息感知与智能服务重点实验室，陕西杨凌 712100

收稿日期:2022-03-21 出版日期:2022-06-30
基金项目:
国家重点研发计划项目(2017YFD0701603);陕西省技术创新引导计划(2022QFY11-02)
作者简介:王政（1998－），男，硕士研究生，研究方向为智能化检测与技术。E-mail：wang_zheng@nwafu.edu.cn
通信作者: 宋怀波（1980－），男，博士，教授，研究方向为智能化检测与技术。E-mail：songhuaibo@nwafu.edu.cn

Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior

WANG Zheng¹^,²^,³(), SONG Huaibo¹^,²^,³(), WANG Yunfei¹^,²^,³, HUA Zhixin¹^,²^,³, LI Rong¹^,²^,³, XU Xingshi¹^,²^,³

^1.College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
^2.Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
^3.Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China

Received:2022-03-21 Online:2022-06-30
Foundation items:National Key Research and Development Program of China (2017YFD0701603); Shaanxi Province Technology Innovation Guidance Program (2022QFY11-02)
About author:WANG Zheng, E-mail：wang_zheng@nwafu.edu.cn
Corresponding author:宋怀波, E-mail：songhuaibo@nwafu.edu.cn

摘要/Abstract

摘要：

奶牛运动行为蕴含着诸多健康信息。信息化、智能化技术的应用有助于养殖场及时掌握奶牛健康状况，提高养殖效率。本文主要针对奶牛运动行为智能监测技术的研究进展予以分析，首先对奶牛基本运动（躺卧、行走、站立）、发情、呼吸、反刍及跛行等行为的监测意义进行阐述，明确了奶牛行为监测的必要性；其次按照时间顺序分别从接触式监测方法和非接触式监测方法两方面综述了国内外相关研究现状，对相关研究的原理及成果进行详细介绍，并进行了分类总结；对奶牛行为监测产业发展现状进行了分析，介绍了国外主流牧场自动化设备供应商主营业务及代表产品；之后分别提出了当前接触式和非接触式奶牛运动行为监测方法的问题与挑战。最后，针对相关关键技术的发展趋势进行了展望。

关键词: 奶牛, 运动行为, 健康状况, 智能化监测, 智慧畜牧

Abstract:

The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy, and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization.

Key words: dairy cows, motion behavior, health condition, intelligent monitoring, smart animal husbandry

中图分类号:

王政, 宋怀波, 王云飞, 华志新, 李嵘, 许兴时. 奶牛运动行为智能监测研究进展与技术趋势[J]. 智慧农业(中英文), 2022, 4(2): 36-52.

WANG Zheng, SONG Huaibo, WANG Yunfei, HUA Zhixin, LI Rong, XU Xingshi. Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior[J]. Smart Agriculture, 2022, 4(2): 36-52.

图/表 7

表1

接触式奶牛行为监测相关研究

传感器类型	特征类型	分类算法	监测行为类型	准确率	来源	年份
加速度	颈部运动数据	K-Means	静止、慢走、快跑、爬跨	——	尹令等^［12］	2010
加速度，位置	蹄部运动数据，位置坐标	多分类 BP-AdaBoost	采食、躺卧、静止站立、躺下、起身、正常行走、主动行走	多数80%以上	Wang等^［13］	2018
加速度	颈部运动数据	SVM^①	站立、躺卧、采食、行走	90.24%	Hoang等^［14］	2018
加速度	侧腹运动数据	随机森林	采食、移动（行走或轻微移动）、反刍、休息	75.90%	Balasso等^［15］	2021
加速度，位置	颈部运动数据，位置坐标	BP神经网络	发情	95.46%	Wang等^［16］	2022
加速度	下颌运动数据	KNN^②	采食和反刍	采食：92.80% 反刍：93.70%	Shen等^［17］	2019
加速度	颈部运动数据	——	放牧和反刍	——	Iqbal等^［18］	2021
惯性测量单元	蹄部运动数据	SVM	跛行	91.10%	Haladjian等^［19］	2017
计步器	活动量	——	妊娠后期活动量	——	蒋晓新等^［20］	2014
计步器	活动量	——	蹄病	——	蒋晓新等^［21］	2014
计步器	活动量	SVM	发情	98.90%	谭益等^［22］	2018
计步器	活动量	KNN	跛行	87.00%	Taneja等^［23］	2020
压力	呼吸时腹部规律性起伏	——	呼吸	——	Eigenberg等^［24］	2000
压力	呼吸时鼻腔与周围环境的压力差	——	呼吸	与人工计数具有较高相关性	Strutzke等^［25］	2018
压力	咀嚼时产生规律的压力变化	——	反刍	与人工计数具有较高相关性	Braun等^［26］	2013
压力	咀嚼时产生规律的压力变化	——	采食和反刍	与称重槽测定结果具有较高相关性	Pahl等^［27］	2016
压力	足底压力分布情况	——	跛行	——	杨丽娟等^［28］	2016
电阻	阴道电阻值	——	发情	精度为± 2.00%	刘忠超和何东健^［29］	2019
振动，姿态，温度	活动量，静卧时间，体温	学习矢量量化神经网络	发情	预测准确率70.00%以上	田富洋等^［30］	2013
声音	声音识别出下颌运动	基于多层感知机的自下而上觅食活动识别器算法	采食和反刍	F₁分值均高于0.75	Chelotti等^［31］	2020
温度	奶牛鼻孔附近环境温度	——	呼吸	与人工计数无统计学差异	Milan等^［32］	2016

表1

表2

非接触式奶牛行为监测相关研究

方法	特征类型	分类算法	行为类型	准确率	文献	年份
传统视频图像分析	呼吸时腹部规律起伏	光流法	呼吸	95.68%	赵凯旋等^［33］	2014
	爬跨过程包围两头牛的边界框长度	——	爬跨	0.33%（假阳性率）	Tsai和Huang^［34］	2014
	蹄肢运动曲线	K-Means	跛行	91.15%	Zhao和He^［35］	2014
	牛的质心和轮廓	基于结构相似度的聚类算法	躺卧、站立、行走、奔跑	97. 32%	何东健等^［36］	2016
	爬跨时两头奶牛最小包围盒之间的相交面积	——	爬跨	80.00%	顾静秋等^［37］	2017
	反刍时嘴部区域质心轨迹	均值漂移	反刍	92.03%	Chen等^［38］	2017
	步态特征	基于共轭梯度追踪算法的稀疏超完备词典学习方法	跛行	92.70%	温长吉等^［39］	2018
	反刍时嘴部区域质心轨迹	核相关滤波	反刍	误检率7.72%（双目标）	宋怀波等^［40］	2018
	头颈部轮廓拟合直线斜率	KNN	跛行	93.89%	宋怀波等^［41］	2018
	爬跨时几何和光流特征	SVM	爬跨	90.90%	Guo等^［42］	2019
	呼吸时腹部规律起伏	Lucas-Kanade稀疏光流法	呼吸	98.58%	宋怀波等^［43］	2019
	牛蹄跟随性	阈值判别	跛行	93.30%	康熙等^［44］	2019
	爬跨过程包围两头牛的最小外接矩形	KNN	爬跨	99.21%	谢忠红等^［45］	2021
基于深度学习的视频图像分析	——	CNN^①	爬跨	98.25%	刘忠超和何东健^［46］	2019
	呼吸时腹部规律起伏	融合Deeplab V3+和Lucas-Kanade稀疏光流法	呼吸	93.04%	Wu等^［47］	2020
	——	融合卷积神经网络和长短期记忆网络	躺卧、站立、行走、饮水、反刍	97.60%	Wu等^［48］	2021
	——	改进YOLOv3	爬跨	99.15%	王少华和何东健^［49］	2021
	——	Rexnet 3D	躺卧、站立、行走	95.00%	Ma等^［50］	2022
	——	3D卷积网络和卷积长短期记忆网络	采食、寻觅、舔舐、行走、站立	90.32%（牛犊）86.67%（成年奶牛）	Qiao等^［51］	2022
	背部曲率	噪声+双向长短期记忆网络	跛行	96.61%	Jiang等^［52］	2022
激光	呼吸时腹部规律起伏	——	呼吸	——	Pastell等^［53］	2007
声学	哞叫声	支持向量数据描述	发情	94.00%	Chung等^［54］	2013
热成像	呼吸时由呼吸气流引起的鼻部区域像素强度值变化	——	呼吸	较人工计数结果相关系数为0.87	Jorquera-Chavez等^［55］	2019

表2

图1

图2

图3

图4

表3

部分国外牧场自动化设备供应商及产品

公司名称	国家	主营业务	部分产品	主要功能	网址
阿菲金（Afimilk）	以色列	智能穿戴，数字奶厅，智能分群，管理软件	项圈计步器AfiCollar	活动量统计，牛号识别，反刍时间，采食时间	http：//www.afimilk.com.cn/
阿菲金（Afimilk）	以色列	智能穿戴，数字奶厅，智能分群，管理软件	脚环计步器AfiActll Tag	活动量统计，牛号识别，躺卧数据统计，舒适度监控，产犊预警	http：//www.afimilk.com.cn/
安乐福（Allflex）	美国	动物识别，智能监控，奶厅整机，智能数据	eSense™Flex耳标	繁育监测，营养监测，健康监测，群组监控，牧场管理	https：//www.allflex.global/cn/our-legacy/
安乐福（Allflex）	美国	动物识别，智能监控，奶厅整机，智能数据	cSense™ Flex项圈	繁育监测，营养监测，健康监测，群组监控，牧场管理	https：//www.allflex.global/cn/our-legacy/
利拉伐（DeLaval）	瑞典	挤奶解决方案，牧场管理系统，奶牛舒适产品，奶牛饲喂产品	自愿挤奶系统 VMS™ V300	自动挤奶，收集挤奶信息	https：//www.delaval.com/zh-cn/
利拉伐（DeLaval）	瑞典	挤奶解决方案，牧场管理系统，奶牛舒适产品，奶牛饲喂产品	自动搅拌推料机器人 OptiDuo™	饲料混合搅拌，移动推料	https：//www.delaval.com/zh-cn/
莱力（Lely）	荷兰	挤奶方案，饲喂方案，牛舍管理，健康管理，大型牧场管理，数据管理分析	Lely Calm犊牛饲喂器	自动喂料，自动调节饲料配量，自动清洁，远程监控	https：//www.lely.com/
莱力（Lely）	荷兰	挤奶方案，饲喂方案，牛舍管理，健康管理，大型牧场管理，数据管理分析	Lely Juno 自动推料机	预定轨迹移动，自动送料	https：//www.lely.com/
COWLAR	美国	可穿戴奶牛监测设备	智能项圈	健康监测，发情周期预测	https：//www.cowlar.com/
Connecterra	荷兰	奶牛健康监测	Ida智能项圈	牛群概况，行为分析，健康统计，生育信息	https：//www.connecterra.io/
博美特（BouMatic）	美国	精准健康管理，智能化挤奶方案，牛奶冷却系统，牧场卫生清洁系统等	GEMINI MILKING ROBOTS挤奶机器人	并排挤奶，自动挤奶	https：//boumatic.com/eu_en/
博美特（BouMatic）	美国	精准健康管理，智能化挤奶方案，牛奶冷却系统，牧场卫生清洁系统等	OPTIFLO™ CF 电子奶泵速度控制器	牛奶冷却输送	https：//boumatic.com/eu_en/
Farmnote	日本	可穿戴奶牛监测设备	Farmnote Color项圈	健康监测，异常预警，智能终端可视化管理	https：//farmnote.jp/en/
加拉格尔（Gallagher）	新西兰	电子围栏，智能称重，牧场管理软件	eShepherd颈带	GPS定位，终端设定虚拟围栏，牛只越界自动警示	https：//www.gallagher.com/
基伊埃（GEA）	德国	挤奶方案，牛奶冷却与存储，卫生清洁方案，饲喂系统，牛群管理系统	CowScout 项圈/脚环	精准定位，发情监测，活动量监测采食及反刍时间监测	https：//www.gea.com/zh/dairy-farming/index.jsp

表3

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奶牛运动行为智能监测研究进展与技术趋势

Research Progress and Technology Trend of Intelligent Morning of Dairy Cow Motion Behavior

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