Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 21-36.doi: 10.12133/j.smartag.2020.2.3.202006-SA003
• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles Next Articles
HAN Shuqing1(), ZHANG Jing1, CHENG Guodong1, PENG Yingqi2, ZHANG Jianhua1, WU Jianzhai1(
)
Received:
2020-06-08
Revised:
2020-07-08
Online:
2020-09-30
Published:
2020-12-09
corresponding author:
Jianzhai WU
E-mail:hanshuqing@caas.cn;wujianzhai@caas.cn
CLC Number:
HAN Shuqing, ZHANG Jing, CHENG Guodong, PENG Yingqi, ZHANG Jianhua, WU Jianzhai. Current State and Challenges of Automatic Lameness Detection in Dairy Cattle[J]. Smart Agriculture, 2020, 2(3): 21-36.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2020.2.3.202006-SA003
Table 1
Assessment criteria of automatic lameness detection system
跛行程度 | 弓背① | 点头② | 牛蹄跟随性③ | 牛蹄侧偏量④ | 步幅 | 行走速度 | 关节灵活性 | 非对称步态 | 负重倾向性 | 采食量下降/% | 产奶量下降/%[ |
---|---|---|---|---|---|---|---|---|---|---|---|
正常 | 站立平直、行走平直 | 头部稳定 | 一致 | 无 | 正常 | 正常 | 关节灵活,正常行走 | 对称 | 四肢均匀承重 | 0 | 0 |
轻度 | 站立平直、行走弓背 | 头部下倾 | 轻微不一致 | 较小 | 正常 | 正常 | 关节略显僵硬,步态异常 | 轻微不对称 | 四肢均匀承重 | 1 | 0 |
中度 | 站立弓背、行走弓背 | 头部下倾 | 不一致 | 较小 | 小 | 缓慢 | 关节僵硬,步态异常 | 不对称 | 跛足对侧肢蹄承重较多,轻微跛足行走 | 3 | 5 |
跛行 | 站立弓背、行走弓背 | 轻微摆头 | 不一致 | 明显 | 小 | 缓慢 | 关节僵硬,不能正常行走 | 不对称 | 跛足对侧肢蹄承重较多,跛足不完全落地 | 7 | 15 |
严重 | 站立弓背、行走弓背 | 明显摆头 | 明显不一致 | 明显 | 小 | 缓慢 | 关节难弯曲,行动受限 | 不对称 | 完全由跛足对侧肢体承重,三肢跳跃前进 | 16 | 36 |
Table 2
Researches on lameness detection based on machine vision
文献 | 年份 | 跛行特征 | 分类算法 | 成像技术 | 召回率/% | 真负率/% | 准确率/% | 成熟度 | 样本量 |
---|---|---|---|---|---|---|---|---|---|
Poursaberi等[ | 2010 | 弓背 | 阈值判别 | 2D | —— | —— | 96.00 | 算法开发 | 184 |
Viazzi等[ | 2013 | 弓背 | 决策树 | 2D | 91.00 | —— | 91.00 | 算法开发 | 8 |
Viazzi等[ | 2014 | 弓背 | 决策树 | 3D | 82.00 | 91.00 | 90.00 | 算法开发 | 273 |
Hertem等[ | 2014 | 弓背 | 多类别逻辑回归、线性回归 | 3D | 47.1~54.9 | 90.4~94.1 | 60.2(5分制)81.2(二分类) | 性能验证 | 186 |
顾静秋等[ | 2017 | 弓背、运动量 | —— | 2D、加速度计 | —— | —— | 80.00 | 算法开发 | 400 |
Jabbar等[ | 2017 | 步态对称性 | SVM | 3D | 100.00 | 75.00 | 95.70 | 性能验证 | 22 |
温长吉等[ | 2018 | 弓背、步态异常 | SVM | 2D | —— | —— | 93.30 | 算法开发 | 500 |
Zhao等[ | 2018 | 行走速度、步态对称性、步幅等 | 决策树 | 2D | 90.25 | 94.74 | 90.18 | 算法开发 | 98 |
Gardenier等[ | 2018 | 牛蹄跟随性、牛蹄侧偏量 | —— | 3D | —— | —— | —— | 算法开发 | 223 |
宋怀波等[ | 2018 | 点头 | KNN | 2D | —— | —— | 93.89 | 算法开发 | 30 |
康熙等[ | 2019 | 牛蹄跟随性 | 阈值判别 | 2D | 93.30 | —— | —— | 算法开发 | 30 |
Jiang等[ | 2019 | 行走速度 | 前景像素统计分析 | 2D | 90.00 | —— | —— | 算法开发 | 16 |
Table 4
Researches on lameness detection based on pressure distribution measuring technology
文献 | 年份 | 跛行特征 | 分类算法 | 召回率/% | 特异性/% | 成熟度 | 样本量/个 |
---|---|---|---|---|---|---|---|
Maertens等[ | 2011 | 牛蹄跟随性、牛蹄侧偏量、步幅、步态对称性、负重倾向性、行走速度 | 线性判别分析 | 76~90 | 86~100 | 性能验证 | 174 |
Pluk等[ | 2012 | 关节灵活性 | 二次判别分析 | —— | —— | 传感器研发 | 75 |
Nuffel等[ | 2013 | 步幅、步态对称性、行走速度、牛蹄跟随性、牛蹄侧偏量 | —— | —— | —— | 决策支持 | 8 |
刘彩霞等[ | 2015 | 负重倾向性、步幅、行走速度、 | —— | —— | —— | 传感器研发 | 8 |
Gucht等[ | 2017 | 肢蹄落地的触痛程度 | —— | —— | —— | 算法开发 | 32 |
Table 5
Researches on wearable sensors based lameness detection
文献 | 时间 | 跛行特征 | 分类算法 | 召回率/% | 特异性/% | 成熟度 | 样本量/个 |
---|---|---|---|---|---|---|---|
Chapinal等[ | 2009 | 牛蹄侧偏量、步幅、步态对称性、负重倾向性、行走速度 | —— | —— | —— | —— | 100 |
Chapinal等[ | 2011 | 步态对称性(加速度幅度、方差) | —— | —— | —— | 传感器研发 | 36 |
Yunta等[ | 2012 | 躺卧行为特征 | —— | —— | —— | 决策支持 | 1290 |
Alsaaod等[ | 2012 | 躺卧行为特征(总躺卧时长、躺卧次数、每次躺卧时长) | SVM | —— | —— | 算法验证阶段 | 30 |
Thorup等[ | 2015 | 躺卧时长、站立时长、散步时长、步频、运动指数 | PCA | —— | —— | 传感器研发 | 348 |
Alsaaod等[ | 2017 | 步态周期、站立相、摆动相;离地时刻、承重时刻加速度 | 阈值判别 | 100 | 100 | 决策支持 | 17/24 |
Haladjian等[ | 2018 | 负重倾向性、步态对称性、步幅 | SVM | 62.5~83.3 | 81.7~97.2 | 传感器研发 | 10 |
Table 6
Behavior analysis for lameness detection
文献 | 年份 | 智能设备 | 特征变量 | 分类算法 | 召回率/% | 真负率/% | 样本量/个 |
---|---|---|---|---|---|---|---|
Hertem等[ | 2013 | 电子项圈、奶流量计 | 日产奶量、颈环监测活动量、反刍时间 | 逻辑回归模型 | 89.0 | 85.0 | 118 |
Kamphuis等[ | 2013 | 称重平台、挤奶机器人、电子脚环 | 体重、产奶量 | 逻辑回归模型 | 56.8 | 80.0 | 292 |
Mol等[ | 2013 | 挤奶机器人、电子脚环、精准饲喂站 | 精料剩余量、产奶量 | 动态线性模型 | 85.5 | 88.8 | 100 |
Grimm等[ | 2019 | 挤奶机器人、电子脚环、精准饲喂站 | 采食时长、采食次数、躺卧时长、躺卧次数、活动量 | 弹性网络 | 94.0 | 81.0 | 100 |
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