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Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Published online by Cambridge University Press:  03 August 2015

T. Van Hertem ,
C. Bahr ,
A. Schlageter Tello ,
S. Viazzi ,
M. Steensels ,
C. E. B. Romanini ,
C. Lokhorst ,
E. Maltz ,
I. Halachmi  and
D. Berckmans
T. Van Hertem
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium Institute of Agricultural Engineering, Agricultural Research Organization (ARO) – the Volcani Center, PO Box 6, IL-50250 Bet Dagan, Israel
C. Bahr
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium
A. Schlageter Tello
Affiliation:
Wageningen UR Livestock Research, PO Box 338, NL-6700AH Wageningen, The Netherlands
S. Viazzi
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium
M. Steensels
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium
C. E. B. Romanini
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium
C. Lokhorst
Affiliation:
Wageningen UR Livestock Research, PO Box 338, NL-6700AH Wageningen, The Netherlands
E. Maltz
Affiliation:
Institute of Agricultural Engineering, Agricultural Research Organization (ARO) – the Volcani Center, PO Box 6, IL-50250 Bet Dagan, Israel
I. Halachmi
Affiliation:
Institute of Agricultural Engineering, Agricultural Research Organization (ARO) – the Volcani Center, PO Box 6, IL-50250 Bet Dagan, Israel
D. Berckmans*
Affiliation:
M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Leuven, Belgium
*
† E-mail: daniel.berckmans@biw.kuleuven.be
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Abstract

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Keywords

lameness detectiondairy cowsensor technologymulti-sensingindividual history
Type
Research Article
Information
animal , Volume 10 , Issue 9 , September 2016 , pp. 1525 - 1532
Copyright
© The Animal Consortium 2015 

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References

Alawneh, JI, Laven, RA and Stevenson, MA 2012. Interval between detection of lameness by locomotion scoring and treatment for lameness: a survival analysis. Veterinary Journal 193, 622–625.CrossRefGoogle ScholarPubMed
Becker, J, Steiner, A, Kohler, S, Koller-Bahler, A, Wuthrich, M and Reist, M 2014. Lameness and foot lesions in Swiss dairy cows: II. Risk factors. Schweizer Archiv Fur Tierheilkunde 156, 79–89.Google Scholar
Blackie, N, Bleach, ECL, Amory, JR and Scaife, JR 2013. Associations between locomotion score and kinematic measures in dairy cows with varying hoof lesion types. Journal of Dairy Science 96, 3564–3572.CrossRefGoogle ScholarPubMed
Bradley, A 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159.CrossRefGoogle Scholar
Brehme, U, Stollberg, U, Holz, R and Schleusener, T 2008. ALT pedometer – new sensor-aided measurement system for improvement in oestrus detection. Computers and Electronics in Agriculture 62, 73–80.CrossRefGoogle Scholar
Bruijnis, M, Beerda, B, Hogeveen, H and Stassen, E 2012. Assessing the welfare impact of foot disorders in dairy cattle by a modeling approach. Animal 6, 962–970.CrossRefGoogle ScholarPubMed
Chapinal, N, de Passille, A, Rushen, J and Wagner, S 2010. Automated methods for detecting lameness and measuring analgesia in dairy cattle. Journal of Dairy Science 93, 2007–2013.Google Scholar
de Mol, RM, Andre, G, Bleumer, EJB, van der Werf, JTN, de Haas, Y and van Reenen, CG 2013. Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows. Journal of Dairy Science 96, 3703–3712.Google Scholar
Fabian, J, Laven, RA and Whay, HR 2014. The prevalence of lameness on New Zealand dairy farms: a comparison of farmer estimate and locomotion scoring. The Veterinary Journal 201, 31–38.CrossRefGoogle ScholarPubMed
Fawcett, T 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874.Google Scholar
Flower, F and Weary, D 2009. Gait assessment in dairy cattle. Animal 3, 87–95.CrossRefGoogle ScholarPubMed
Hoffman, A, Moore, D, Vanegas, J and Wenz, J 2014. Association of abnormal hind-limb postures and back arch with gait abnormality in dairy cattle. Journal of Dairy Science 97, 2178–2185.CrossRefGoogle ScholarPubMed
Hosmer, DW and Lemeshow, S 2000. Applied logistic regression. John Wiley & Sons, New York, NY, USA.Google Scholar
Ito, K, von Keyserlingk, MAG, LeBlanc, SJ and Weary, DM 2010. Lying behavior as an indicator of lameness in dairy cows. Journal of Dairy Science 93, 3553–3560.Google Scholar
Kamphuis, C, Frank, E, Burke, J, Verkerk, G and Jago, J 2013. Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness. Journal of Dairy Science 96, 7043–7053.Google Scholar
Kramer, E, Cavero, D, Stamer, E and Krieter, J 2009. Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livestock Science 125, 92–96.Google Scholar
Leach, K, Whay, H, Maggs, C, Barker, Z, Paul, E, Bell, A and Main, D 2010. Working towards a reduction in cattle lameness: 2. Understanding dairy farmers’ motivations. Research in Veterinary Science 89, 318–323.Google Scholar
Main, D, Barker, Z, Leach, K, Bell, N, Whay, H and Browne, W 2010. Sampling strategies for monitoring lameness in dairy cattle. Journal of Dairy Science 93, 1970–1978.Google Scholar
McCulloch, CE and Neuhaus, JM 2005. Generalized linear mixed models. In Encyclopedia of biostatistics, 2nd edition ed. (P Armitage and T Colton), pp. 2085–2089. John Wiley & Sons Ltd., New York, NY, USA.Google Scholar
Miekley, B, Stamer, E, Traulsen, I and Krieter, J 2013. Implementation of multivariate cumulative sum control charts in mastitis and lameness monitoring. Journal of Dairy Science 96, 5723–5733.Google Scholar
Pluk, A, Bahr, C, Leroy, T, Poursaberi, A, Song, X, Vranken, E, Maertens, W, Van Nuffel, A and Berckmans, D 2010. Evaluation of step overlap as an automatic measure in dairy cow locomotion. Transactions of the Asabe 53, 1305–1312.Google Scholar
Pluk, A, Bahr, C, Poursaberi, A, Maertens, W, van Nuffel, A and Berckmans, D 2012. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques. Journal of Dairy Science 95, 1738–1748.Google Scholar
Poursaberi, A, Bahr, C, Pluk, A, Van Nuffel, A and Berckmans, D 2010. Real-time automatic lameness detection based on back posture extraction in dairy cattle: shape analysis of cow with image processing techniques. Computers and Electronics in Agriculture 74, 110–119.Google Scholar
Romanini, CEB, Bahr, C, Viazzi, S, Van Hertem, T, Schlageter Tello, A, Halachmi, I, Lokhorst, C and Berckmans, D 2013. Application of image based filtering to improve the performance of an automated lameness detection system for dairy cows. Proceedings of the 2013 ASABE Annual International Meeting, 21–24 July, Kansas City, Missouri, USA. Paper No. 131620675.Google Scholar
Rutten, C, Velthuis, A, Steeneveld, W and Hogeveen, H 2013. Invited review: sensors to support health management on dairy farms. Journal of Dairy Science 96, 1928–1952.Google Scholar
Schlageter-Tello, A, Bokkers, E, Koerkamp, P, Van Hertem, T, Viazzi, S, Romanini, C, Halachmi, I, Bahr, C, Berckmans, D and Lokhorst, K 2014a. Effect of merging levels of locomotion scores for dairy cows on intra- and interrater reliability and agreement. Journal of Dairy Science 97, 5533–5542.Google Scholar
Schlageter-Tello, A, Bokkers, E, Koerkamp, P, Van Hertem, T, Viazzi, S, Romanini, C, Halachmi, I, Bahr, C, Berckmans, D and Lokhorst, K 2014b. Manual and automatic locomotion scoring systems in dairy cows: a review. Preventive Veterinary Medicine 116, 12–25.Google Scholar
Sprecher, D, Hostetler, D and Kaneene, J 1997. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology 47, 1179–1187.CrossRefGoogle ScholarPubMed
Thomas, HJ, Pacheco, MG, Bell, NJ, Mason, C, Whay, HR, Maxwell, O, Archer, SC, Remnant, J, Bollard, N, Sleeman, P and Huxley, JN 2013. Investigation of early and effective treatment interventions for claw horn lesions in UK dairy cows. Cattle Practice 21, 166.Google Scholar
Van Hertem, T, Maltz, E, Antler, A, Romanini, CEB, Viazzi, S, Bahr, C, Schlageter-Tello, A, Lokhorst, C, Berckmans, D, Halachmi, I 2013. Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity. Journal of Dairy Science 96, 4286–4298.CrossRefGoogle ScholarPubMed
Van Hertem, T, Viazzi, S, Steensels, M, Maltz, E, Antler, A, Alchanatis, V, Schlageter-Tello, A, Lokhorst, K, Romanini, E, Bahr, C, Berckmans, D, Halachmi, I 2014. Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering 119, 108–116.CrossRefGoogle Scholar
Van Nuffel, A, Sprenger, M, Tuyttens, FAM and Maertens, W 2009. Cow gait scores and kinematic gait data: can people see gait irregularities? Animal Welfare 18, 433–439.Google Scholar
Van Nuffel, A, Vangeyte, J, Mertens, KC, Pluym, L, De Campeneere, S, Saeys, W, Opsomer, G and Van Weyenberg, S 2013. Exploration of measurement variation of gait variables for early lameness detection in cattle using the GAITWISE. Livestock Science 156, 88–95.Google Scholar
Viazzi, S, Bahr, C, Schlageter-Tello, A, Van Hertem, T, Romanini, CEB, Pluk, A, Halachmi, I, Lokhorst, C and Berckmans, D 2013. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. Journal of Dairy Science 96, 257–266.Google Scholar
Viazzi, S, Bahr, C, Van Hertem, T, Schlageter-Tello, A, Romanini, CEB, Halachmi, I, Lokhorst, C and Berckmans, D 2014. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Computers and Electronics in Agriculture 100, 139–147.Google Scholar