Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 36-52.doi: 10.12133/j.smartag.SA202203011
• Topic--Smart Animal Husbandry Key Technologies and Equipment • Previous Articles Next Articles
WANG Zheng1,2,3(), SONG Huaibo1,2,3(), WANG Yunfei1,2,3, HUA Zhixin1,2,3, LI Rong1,2,3, XU Xingshi1,2,3
Received:
2022-03-21
Online:
2022-06-30
corresponding author:
宋怀波, E-mail:songhuaibo@nwafu.edu.cn
About author:
WANG Zheng, E-mail:wang_zheng@nwafu.edu.cn
Supported by:
CLC Number:
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.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202203011
Table 1
Researches on contact behavior monitoring of dairy cows
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[ | 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[ | 2018 |
加速度 | 颈部运动数据 | SVM① | 站立、躺卧、采食、行走 | 90.24% | Hoang等[ | 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[ | 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[ | 2022 |
加速度 | 下颌运动数据 | KNN② | 采食和反刍 | 采食:92.80% 反刍:93.70% | Shen等[ | 2019 |
加速度 | 颈部运动数据 | —— | 放牧和反刍 | —— | Iqbal等[ | 2021 |
惯性测量单元 | 蹄部运动数据 | SVM | 跛行 | 91.10% | Haladjian等[ | 2017 |
计步器 | 活动量 | —— | 妊娠后期活动量 | —— | 蒋晓新等[ | 2014 |
计步器 | 活动量 | —— | 蹄病 | —— | 蒋晓新等[ | 2014 |
计步器 | 活动量 | SVM | 发情 | 98.90% | 谭益等[ | 2018 |
计步器 | 活动量 | KNN | 跛行 | 87.00% | Taneja等[ | 2020 |
压力 | 呼吸时腹部规律性起伏 | —— | 呼吸 | —— | Eigenberg等[ | 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[ | 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[ | 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[ | 2016 |
压力 | 足底压力分布情况 | —— | 跛行 | —— | 杨丽娟等[ | 2016 |
电阻 | 阴道电阻值 | —— | 发情 | 精度为± 2.00% | 刘忠超和 何东健[ | 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[ | 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[ | 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[ | 2016 |
Table 2
Researches on non-contact behavior monitoring of dairy cows
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[ | 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[ | 2014 | |
蹄肢运动曲线 | K-Means | 跛行 | 91.15% | Zhao和He[ | 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[ | 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[ | 2017 | |
反刍时嘴部区域质心轨迹 | 均值漂移 | 反刍 | 92.03% | Chen等[ | 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[ | 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[ | 2018 | |
头颈部轮廓拟合直线斜率 | KNN | 跛行 | 93.89% | 宋怀波等[ | 2018 | |
爬跨时几何和光流特征 | SVM | 爬跨 | 90.90% | Guo等[ | 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[ | 2019 | |
牛蹄跟随性 | 阈值判别 | 跛行 | 93.30% | 康熙等[ | 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[ | 2021 | |
基于深度学习的视频图像分析 | —— | CNN① | 爬跨 | 98.25% | 刘忠超和 何东健[ | 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[ | 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[ | 2021 | |
—— | 改进YOLOv3 | 爬跨 | 99.15% | 王少华和 何东健[ | 2021 | |
—— | Rexnet 3D | 躺卧、站立、行走 | 95.00% | Ma等[ | 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[ | 2022 | |
背部曲率 | 噪声+双向长短期记忆网络 | 跛行 | 96.61% | Jiang等[ | 2022 | |
激光 | 呼吸时腹部规律起伏 | —— | 呼吸 | —— | Pastell等[ | 2007 |
声学 | 哞叫声 | 支持向量数据描述 | 发情 | 94.00% | Chung等[ | 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[ | 2019 |
Table 3
Part of international livestock farm automation equipment suppliers and products
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/ |
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 | ||||
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/ |
cSense™ Flex项圈 | |||||
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/ |
自动搅拌推料机器人 OptiDuo™ | 饲料混合搅拌,移动推料 | ||||
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/ |
Lely Juno 自动推料机 | 预定轨迹移动,自动送料 | ||||
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/ |
OPTIFLO™ CF 电子奶泵速度控制器 | 牛奶冷却输送 | ||||
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 |
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