Current State and Challenges of Automatic Lameness Detection in Dairy Cattle

doi:10.12133/j.smartag.2020.2.3.202006-SA003

Abstract

Abstract:

Lameness in dairy cattle could cause significant economic losses to the dairy industry. Detection of lameness in a timely manner is critical to the high-quality development of dairy industry. The traditional method is visual locomotion scoring by dairy farmers, which is low efficiency, high cost and subjective. The demand for automated lameness detection is increasing. The review was conducted to find out the current state and challenges of automatic lameness detection technology development and to learn from the latest findings. The current automatic lameness detection systems were reviewed in this paper mainly rely on five technologies or combinations thereof, including machine vision, pressure distribution measuring system, wearable sensor system, behavior analysis and classification; the principle, function and features of these technologies were analyzed. Machine vision technique is to extract feature variables (e.g. back arch, head bob, abduction, stride length, walking speed, temperature, etc.) from video recordings of cattle movement by image processing. Pressure distribution measuring system contains an array of load cells to sense gait variables, when dairy cattle are walking by. By using accelerometer with high frequency data collection, the gait cycle parameters can be extracted and used for lameness detection. By using wearable devices, the number of lying/standing bouts and their duration, the total time spent lying, standing and ruminating per day can be recorded for individual cattle. The lameness can also be detected by behavior analysis. Currently, most of these studies were in the stage of sensor development or validation of algorithm. A few studies were in the stage of validation of performance and decision support with early warning system. The challenges to apply automatic lameness detection system in dairy farm includes the difficulties of acquiring high quality data of lameness features, lack of techniques to detect early lameness, identification errors caused by individual gait differences among dairy cattle, difficulties to function well in unstructured environment and difficulties to evaluate the benefits. To accelerate the development of automatic lameness detection systems, recommendations are proposed as follows: ①promoting lameness data sharing and data exchange among dairy farms; ②developing individual-based lameness classification model; ③developing multifunctional smart station which can detect lameness, measure body condition score, weighing, etc; ④evaluating the significance of automatic lameness detection to the dairy industry from the perspective of animal welfare, environment and food safety.

Key words: lameness detection, behavior analysis, precision livestock farming, machine vision, automated dection, pressure distribution, wearable sensor system

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.

Figures/Tables 8

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References 95

1	TADICH N, FLOR E, GREEN L. Associations between hoof lesions and locomotion score in 1098 unsound dairy cows[J]. The Veterinary Journal, 2010, 184(1): 60-65.
2	邵军, 林为民, 孙新文, 等. 石河子地区奶牛肢蹄病类型和发病情况调查分析[J]. 中国奶牛, 2017(1): 30-33.
	SHAO J, LIN W, SUN X, et al. Investigation and analysis of types and incidence of limb and hoof diseases of dairy cows in Shihezi area[J]. China Dairy Cattle, 2017(1): 30-33.
3	ÖHM A. A systematic review and meta-analyses of risk factors associated with lameness in dairy cows[D]. Munich: University of Munich, 2019.
4	李小杉, 杨丰利. 奶牛肢蹄病对繁殖性能的影响[J]. 中国畜牧兽医, 2014(5): 248-251.
	LI X, YANG F. Effect of lameness on reproductive performance in dairy cows[J]. China Animal Husbandry & Veterinary Medicine, 2014(5): 248-251.
5	BOOTH C J, WARNICK L D, GRÖHN Y T, et al. Effect of lameness on culling in dairy cows[J]. Journal of Dairy Science, 2004, 87(12): 4115-4122.
6	BLACKIE N, AMORY J, BLEACH E, et al. The effect of lameness on lying behaviour of zero grazed Holstein dairy cattle[J]. Applied Animal Behaviour Science, 2011, 134(3): 85-91.
7	HUXLEY J N. Impact of lameness and claw lesions in cows on health and production[J]. Livestock Science, 2013, 156(1): 64-70.
8	CHEN W, WHITE E, HOLDEN N M. The effect of lameness on the environmental performance of milk production by rotational grazing[J]. Journal of Environmental Management, 2016, 172: 143-150.
9	AVAN NUFFEL, ZWERTVAEGHER I, SVAN WEYENBERG, et al. Lameness detection in dairy cows: Part 2. use of sensors to automatically register changes in locomotion or behavior[J]. Animals, 2015, 5(3): 861-885.
10	SPRECHER D J, HOSTETLER D E, KANEENE J B. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance [J]. Theriogenology, 1997, 47(6): 1179-1187.
11	HULSEN J. Cow signals checkbook working on health, production and welfare[M]. Zutphen: Roodbont Publisher, 2016.
12	NORTON T, BERCKMANS D. Developing precision livestock farming tools for precision dairy farming[J]. Animal Frontiers, 2017, 7(1): 18-23.
13	ALSAAOD M, FADUL M, STEINER A. Automatic lameness detection in cattle[J]. The Veterinary Journal, 2019, 246: 35-44.
14	NASIRAHMADI A, EDWARDS S A, STURM B. Implementation of machine vision for detecting behaviour of cattle and pigs[J]. Livestock Science, 2017, 202: 25-38.
15	POURSABERI A, BAHR C, PLUK A, et al. Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques[J]. Computers and Electronics in Agriculture, 2010, 74(1): 110-119.
16	顾静秋, 王志海, 高荣华, 等. 基于融合图像与运动量的奶牛行为识别方法[J]. 农业机械学报, 2017, 48(6): 145-151.
	GU J, WANG Z, GAO R, et al. Recognition method of cow behavior based on combination of image and activities[J]. Transactions of the CSAM, 2017, 48(6): 145-151.
17	VIAZZI S, BAHR C, TVAN HERTEM, et al. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows[J]. Computers and Electronics in Agriculture, 2014, 100: 139-147.
18	VIAZZI S, BAHR C, SCHLAGETER-TELLO A, et al. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle[J]. Journal of Dairy Science, 2013, 96(1): 257-266.
19	宋怀波, 姜波, 吴倩, 等. 基于头颈部轮廓拟合直线斜率特征的奶牛跛行检测方法[J]. 农业工程学报, 2018, 34(15): 190-199.
	SONG H, JIANG B, WU Q, et al. Detection of dairy cow lameness based on fitting line slope feature of head and neck outline[J]. Transactions of the CSAE, 2018, 34(15): 190-199.
20	康熙, 张旭东, 刘刚, 等. 基于机器视觉的跛行奶牛牛蹄定位方法[J]. 农业机械学报, 2019(S1): 276-282.
	KANG X, ZHANG X, LIU G, et al. Hoof location method of lame dairy cows based on machine vision[J]. Transactions of the CSAM, 2019(S1): 276-282.
21	WU D, WU Q, YIN X, et al. Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector[J]. Biosystems Engineering, 2020, 189: 150-163.
22	温长吉, 张金凤, 李卓识, 等. 改进稀疏超完备词典方法识别奶牛跛足行为[J]. 农业工程学报, 2018(18): 219-227.
	WEN C, ZHANG J, LI Z, et al. Behavior recognition of lameness in dairy cattle by improved sparse overcomplete dictionary method[J]. Transactions of the CSAE, 2018, 34(18): 219-227.
23	ZHAO K, BEWLEY J M, HE D, et al. Automatic lameness detection in dairy cattle based on leg swing analysis with an image processing technique[J]. Computers and Electronics in Agriculture, 2018, 148: 226-236.
24	PLUK A, BAHR C, POURSABERI A, et al. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques[J]. Journal of Dairy Science, 2012, 95(4): 1738-1748.
25	TVAN HERTEM, VIAZZI S, STEENSELS M, et al. Automatic lameness detection based on consecutive 3D-video recordings[J]. Biosystems Engineering, 2014, 119: 108-116.
26	ABDUL JABBAR K, HANSEN M F, SMITH M L, et al. Early and non-intrusive lameness detection in dairy cows using 3-dimensional video[J]. Biosystems Engineering, 2017, 153: 63-69.
27	GARDENIER J, UNDERWOOD J, CLARK C. Object detection for cattle cait tracking[C]// 2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway New York, USA: IEEE, 2018: 2206-2213.
28	JIANG B, SONG H, HE D. Lameness detection of dairy cows based on a double normal background statistical model[J]. Computers and Electronics in Agriculture, 2019, 158: 140-149.
29	宋怀波, 阴旭强, 吴頔华, 等. 基于自适应无参核密度估计算法的运动奶牛目标检测[J]. 农业机械学报, 2019, 50(5): 196-204.
	SONG H, YIN X, WU D, et al. Detection of moving cows based on adaptive kernel density estimation algorithm[J]. Transactions of the CSAM, 2019, 50(5): 196-204.
30	JIANG B, YIN X, SONG H. Single-stream long-term optical flow convolution network for action recognition of lameness dairy cow[J]. Computers and Electronics in Agriculture, 2020, 175: ID 105536.
31	O'MAHONY N, CAMPBELL S, CARVALHO A, et al. 3D vision for precision dairy farming[C]// 6th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture. Amsterdam, Netherlands: Elsevier B.V., 2019: 312-317.
32	ABDUL JABBAR K. 3D video based detection of early lameness in dairy cattle[D]. Bristol: University of the West of England, 2017.
33	HANSEN M F, SMITH M L, SMITH L N, et al. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device[J]. Computers in Industry, 2018, 98: 14-22.
34	YANMAZ L E, ZAFER O, ELIF D J J O A, et al. Instrumentation of thermography and its applications in horses[J]. Journal of Animal and Veterinary Advances,2007, 6(7): 858-862.
35	刘烨虹, 刘修林, 王福杰, 等. 红外热成像技术在畜禽疾病检测中的应用[J]. 中国家禽, 2018, 40(5): 70-72.
	LIU Y, LIU X, WANG F, et al. Application of infrared thermal imaging technology in the detection of livestock and poultry diseases[J]. China Poultry, 2018, 40(5): 70-72.
36	WILHELM K, WILHELM J, FÜRLL M. Use of thermography to monitor sole haemorrhages and temperature distribution over the claws of dairy cattle[J]. Veterinary Record, 2015, 176(6): 146-153.
37	ALSAAOD M, SCHAEFER A L, BÜSCHER W, et al. The role of infrared thermography as a non-invasive tool for the detection of lameness in cattle[J]. Sensors (Basel, Switzerland), 2015, 15(6): 14513-14525.
38	HARRIS-BRIDGE G, YOUNG L, HANDEL I, et al. The use of infrared thermography for detecting digital dermatitis in dairy cattle: What is the best measure of temperature and foot location to use?[J]. The Veterinary Journal, 2018, 237: 26-33.
39	LOKESH B D S, JEYAKUMAR S, JITENDRA V P, et al. Monitoring foot surface temperature using infrared thermal imaging for assessment of hoof health status in cattle: A review[J]. Journal of Thermal Biology, 2018, 78: 10-21.
40	DALBETH A. Development of an automatic lameness detection system for dairy cattle[D]. Manawatu: Massey University, 2016.
41	MCNEIL B M. The use of an embedded microcomputer-based force plate system for accurate sow lameness identification[D]. Ames: Iowa State University, 2015.
42	AVAN NUFFEL, ZWERTVAEGHER I, PLUYM L, et al. Lameness detection in dairy cows: Part 1. How to distinguish between non-lame and lame cows based on differences in locomotion or behavior[J]. Animals, 2015, 5(3): 838-860.
43	CHAPINAL N, DE PASSILLÉ A M, RUSHEN J. Weight distribution and gait in dairy cattle are affected by milking and late pregnancy[J]. Journal of Dairy Science, 2009, 92(2): 581-588.
44	CHAPINAL N, DE PASSILLÉ A M, RUSHEN J, et al. Automated methods for detecting lameness and measuring analgesia in dairy cattle[J]. Journal of Dairy Science, 2010, 93(5): 2007-2013.
45	MAERTENS W, VANGEYTE J, BAERT J, et al. Development of a real time cow gait tracking and analysing tool to assess lameness using a pressure sensitive walkway: The GAITWISE system[J]. Biosystems Engineering, 2011, 110(1): 29-39.
46	AVAN NUFFEL, VANGEYTE J, MERTENS K C, et al. Exploration of measurement variation of gait variables for early lameness detection in cattle using the GAITWISE[J]. Livestock Science, 2013, 156(1): 88-95.
47	刘彩霞, 张永, 杨丽娟, 等. 基于三维应力对跛行奶牛蹄部参数的提取[J]. 安徽农业科学, 2015,43(36): 14-17.
	LIU C, ZHANG Y, YANG L, et al. Parameter extraction of lameness cows based on 3-dimensional reaction force[J]. Journal of Anhui Agricultural Sciences, 2015,43(36): 14-17.
48	DE GUCHT TVAN, SAEYS W, SVAN WEYENBERG, et al. Automatically measured variables related to tenderness of hoof placement and weight distribution are valuable indicators for lameness in dairy cows[J]. Applied Animal Behaviour Science, 2017, 189: 13-22.
49	CHAPINAL N, DE PASSILLÉ A M, WEARY D M, et al. Using gait score, walking speed, and lying behavior to detect hoof lesions in dairy cows[J]. Journal of Dairy Science, 2009, 92(9): 4365-4374.
50	THORUP V M, NASCIMENTO O FDO, SKJØTH F, et al. Short communication: Changes in gait symmetry in healthy and lame dairy cows based on 3-dimensional ground reaction force curves following claw trimming[J]. Journal of Dairy Science, 2014, 97(12): 7679-7684.
51	VOLKMANN N, KULIG B, KEMPER N. Using the footfall sound of dairy cows for detecting claw lesions[J]. Animals, 2019, 9(3): ID 78.
52	ZILLNER J C, TÜCKING N, PLATTES S, et al. Using walking speed for lameness detection in lactating dairy cows[J]. Livestock Science, 2018, 218: 119-123.
53	LIU J, DYER R M, NEERCHAL N K, et al. Diversity in the magnitude of hind limb unloading occurs with similar forms of lameness in dairy cows[J]. Journal of Dairy Research, 2011, 78(2): 168-177.
54	刘德环, 张永, 苏力德, 等. 基于MATLAB GUI的奶牛早期跛行识别数据处理系统的设计[J]. 黑龙江畜牧兽医, 2017(23): 10-13, 284.
	LIU D, ZHANG Y, SU L, et al. Data processing system for early lameness identification of cows based on MATLAB GUI[J]. Heilongjiang Animal Science and Veterinary Medicine, 2017(23): 10-13, 284.
55	杨丽娟, 徐彬腾, 刘德环, 等. 基于SMC控制器的奶牛步态模拟装置的设计[J]. 黑龙江畜牧兽医, 2017(23): 1-5, 281-282.
	YANG L, XU B, LIU D, et al. Design of cow gait simulator based on SMC controller[J]. Heilongjiang Animal Science and Veterinary Medicine, 2017(23): 1-5, 281-282.
56	杨丽娟, 张永, 刘德环, 等. 基于压力分布测量系统的奶牛跛行早期识别[J]. 农业机械学报, 2016, 47(S1): 426-432.
	YANG L, ZHANG Y, LIU D, et al. Early recognition for dairy cow lameness based on pressure distribution measurement system[J]. Transactions of the CSAM, 2016, 47(S1): 426-432.
57	DE GUCHT TVAN, SAEYS W, SVAN WEYENBERG, et al. Automatic cow lameness detection with a pressure mat: Effects of mat length and sensor resolution[J]. Computers and Electronics in Agriculture, 2017, 134: 172-180.
58	於少文, 孔繁涛, 张建华, 等. 可穿戴设备技术在奶牛养殖中的应用及发展趋势[J]. 中国农业科技导报, 2016, 18(5): 102-110.
	YU S, KONG F, ZHANG J, et al. Application and development trend of wearable devices technology in dairy farming[J]. Journal of Agricultural Science and Technology, 2016, 18(5): 102-110.
59	ALSAAOD M, NIEDERHAUSER J J, BEER G, et al. Development and validation of a novel pedometer algorithm to quantify extended characteristics of the locomotor behavior of dairy cows[J]. Journal of Dairy Science, 2015, 98(9): 6236-6242.
60	PENG Y, KONDO N, FUJIURA T, et al. Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units[J]. Computers and Electronics in Agriculture, 2019, 157: 247-253.
61	ECKELKAMP E A. Invited review: Current state of wearable precision dairy technologies in disease detection[J]. Applied Animal Science, 2019, 35(2): 209-220.
62	王俊, 谭骥, 张海洋, 等. 基于无线传感器网络的奶牛运动行为实时监测系统[J]. 家畜生态学报, 2018, 39(10): 45-52.
	WANG J, TAN J, ZHANG H, et al. A real-time monitoring system for cow behavior based on wireless sensor network[J]. Journal of Domestic Animal Ecology, 2018, 39(10): 45-52.
63	王俊, 张海洋, 赵凯旋, 等. 基于最优二叉决策树分类模型的奶牛运动行为识别[J]. 农业工程学报, 2018, 34(18): 202-210.
	WANG J, ZHANG H, ZHAO K, et al. Cow movement behavior classification based on optimal binary decision-tree classification model[J]. Transactions of the CSAE, 2018, 34(18): 202-210.
64	CHAPINAL N, DE PASSILLÉ A M, PASTELL M, et al. Measurement of acceleration while walking as an automated method for gait assessment in dairy cattle[J]. Journal of Dairy Science, 2011, 94(6): 2895-2901.
65	YUNTA C, GUASCH I, BACH A. Short communication: Lying behavior of lactating dairy cows is influenced by lameness especially around feeding time[J]. Journal of Dairy Science, 2012, 95(11): 6546-6549.
66	ALSAAOD M, RÖMER C, KLEINMANNS J, et al. Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior[J]. Applied Animal Behaviour Science, 2012, 142(3): 134-141.
67	THORUP V M, MUNKSGAARD L, ROBERT P E, et al. Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms[J]. Animal, 2015, 9(10): 1704-1712.
68	ALSAAOD M, LUTERNAUER M, HAUSEGGER T, et al. The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies[J]. Journal of Dairy Science, 2017, 100(2): 1417-1426.
69	HALADJIAN J, HAUG J, NÜSKE S, et al. A wearable sensor system for lameness detection in dairy cattle[J]. Multimodal Technologies and Interaction, 2018, 2(2): ID 27.
70	ITO K, M A GVON KEYSERLINGK, LEBLANC S J, et al. Lying behavior as an indicator of lameness in dairy cows[J]. Journal of Dairy Science, 2010, 93(8): 3553-3560.
71	PASTELL M, TIUSANEN J, HAKOJÄRVI M, et al. A wireless accelerometer system with wavelet analysis for assessing lameness in cattle[J]. Biosystems Engineering, 2009, 104(4): 545-551.
72	苏力德, 张永, 王健, 等. 基于改进动态时间规整算法的奶牛步态分割方法[J]. 农业机械学报, 2020,51(7):52-59.
	SU L, ZHANG Y, WANG J, et al. Segmentation method of dairy cattle gait based on improved dynamic time warping algorithm[J]. Transactions of the CSAE, 2020,51(7): 52-59.
73	TANEJA M, BYABAZAIRE J, JALODIA N, et al. Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle[J]. Computers and Electronics in Agriculture, 2020, 171: ID 105286.
74	O'LEARY N W, BYRNE D T, O'CONNOR A H, et al. Invited review: Cattle lameness detection with accelerometers[J]. Journal of Dairy Science, 2020, 103(5): 3895-3911.
75	SINGH N, SINGH A N. Odysseys of agriculture sensors: Current challenges and forthcoming prospects[J]. Computers and Electronics in Agriculture, 2020, 171: ID 105328.
76	MARCHIORO G F, CORNOU C, KRISTENSEN A R, et al. Sows' activity classification device using acceleration data—A resource constrained approach[J]. Computers and Electronics in Agriculture, 2011, 77(1): 110-117.
77	THORUP V M, NIELSEN B L,ROBERT P E, et al. Lameness affects cow feeding but not rumination behavior as characterized from sensor data[J]. Frontiers in Veterinary Science, 2016, 3: ID 37.
78	NORRING M, HÄGGMAN J, SIMOJOKI H, et al. Short communication: Lameness impairs feeding behavior of dairy cows[J]. Journal of Dairy Science, 2014, 97(7): 4317-4321.
79	BEER G, ALSAAOD M, STARKE A, et al. Use of extended characteristics of locomotion and feeding behavior for automated identification of lame dairy cows[J]. PLoS ONE, 2016, 11(5): ID e0155796.
80	TVAN HERTEM, MALTZ E, ANTLER A, et al. Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity[J]. Journal of Dairy Science, 2013, 96(7): 4286-4298.
81	BARKER Z E, VÁZQUEZ DIOSDADO J A, CODLING E A, et al. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle[J]. Journal of Dairy Science, 2018, 101(7): 6310-6321.
82	KAMPHUIS C, FRANK E, BURKE J K, et al. Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness[J]. Journal of Dairy Science, 2013, 96(11): 7043-7053.
83	DE MOL R M, ANDRÉ G, BLEUMER E J B, et al. Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows[J]. Journal of Dairy Science, 2013, 96(6): 3703-3712.
84	GRIMM K, HAIDN B, ERHARD M, et al. New insights into the association between lameness, behavior, and performance in Simmental cows[J]. Journal of Dairy Science, 2019, 102(3): 2453-2468.
85	WARNER D, VASSEUR E, LEFEBVRE D M, et al. A machine learning based decision aid for lameness in dairy herds using farm-based records[J]. Computers and Electronics in Agriculture, 2020, 169: ID 105193.
86	BOELLING D, POLLOTT G E. Locomotion, lameness, hoof and leg traits in cattle I: Phenotypic influences and relationships[J]. Livestock Production Science, 1998, 54(3): 193-203.
87	THOMPSON A J, WEARY D M, BRAN J A, et al. Lameness and lying behavior in grazing dairy cows[J]. Journal of Dairy Science, 2019, 102(7): 6373-6382.
88	NEETHIRAJAN S, TUTEJA S K, HUANG S T, et al. Recent advancement in biosensors technology for animal and livestock health management[J]. Biosensors & Bioelectronics, 2017, 98: 398-407.
89	KANIYAMATTAM K, HERTL J, LHERMIE G, et al. Cost benefit analysis of automatic lameness detection systems in dairy herds: A dynamic programming approach[J]. Preventive Veterinary Medicine, 2020, 178: ID 104993.
90	HOSTIOU N, FAGON J, CHAUVAT S, et al. Impact of precision livestock farming on work and human- animal interactions on dairy farms. A review[J]. Biotechnology Agronomy Society & Environment, 2017, 21(4): 268-275.
91	ECKELKAMP E A, BEWLEY J M. On-farm use of disease alerts generated by precision dairy technology[J]. Journal of Dairy Science, 2020, 103(2): 1566-1582.
92	HALACHMI I, GUARINO M, BEWLEY J, et al. Smart animal agriculture: Application of real-time sensors to improve animal well-being and production[J]. Annual Review of Animal Biosciences, 2019, 7(1): 403-425.
93	PIETTE D, NORTON T, EXADAKTYLOS V, et al. Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance[J]. Animal, 2020, 14(2): 409-417.
94	ROJO-GIMENO C, VOORT MVAN DER, NIEMI J K, et al. Assessment of the value of information of precision livestock farming: A conceptual framework[J]. NJAS—Wageningen Journal of Life Sciences, 2019, 90-91: ID 100311.
95	LOVARELLI D, BACENETTI J, GUARINO M. A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production?[J]. Journal of Cleaner Production, 2020, 262: ID 121409.

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/%^[11]
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

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^[15]	2010	弓背	阈值判别	2D	——	——	96.00	算法开发	184
Viazzi等^[18]	2013	弓背	决策树	2D	91.00	——	91.00	算法开发	8
Viazzi等^[17]	2014	弓背	决策树	3D	82.00	91.00	90.00	算法开发	273
Hertem等^[25]	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^[16]	2017	弓背、运动量	——	2D、加速度计	——	——	80.00	算法开发	400
Jabbar等^[26]	2017	步态对称性	SVM	3D	100.00	75.00	95.70	性能验证	22
温长吉等^[22]	2018	弓背、步态异常	SVM	2D	——	——	93.30	算法开发	500
Zhao等^[23]	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^[27]	2018	牛蹄跟随性、牛蹄侧偏量	——	3D	——	——	——	算法开发	223
宋怀波等^[19]	2018	点头	KNN	2D	——	——	93.89	算法开发	30
康熙等^[20]	2019	牛蹄跟随性	阈值判别	2D	93.30	——	——	算法开发	30
Jiang等^[28]	2019	行走速度	前景像素统计分析	2D	90.00	——	——	算法开发	16

参数	双目相机	结构光	飞行时间
测距精度	cm	um、cm	mm、cm
计算复杂度	高	中/高	低/中
弱光照性能	弱	好	好
强光照性能	好	弱/中	中
变化光照性能	弱	弱	好
黑暗条件工作	否	能	能
校准复杂度	高	高	低
应用成本	低/中	中/高	中

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^[45]	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^[24]	2012	关节灵活性	二次判别分析	——	——	传感器研发	75
Nuffel等^[46]	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^[47]	2015	负重倾向性、步幅、行走速度、	——	——	——	传感器研发	8
Gucht等^[48]	2017	肢蹄落地的触痛程度	——	——	——	算法开发	32

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^[49]	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^[64]	2011	步态对称性（加速度幅度、方差）	——	——	——	传感器研发	36
Yunta等^[65]	2012	躺卧行为特征	——	——	——	决策支持	1290
Alsaaod等^[66]	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^[67]	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^[68]	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^[69]	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