Associations between locomotion score and kinematic measures in dairy cows with varying hoof lesion types

Blackie, Nicola; Bleach, E.C.L.; Amory, Jonathan; Scaife, Jes

doi:10.3168/jds.2012-5597

Cited by 42 publications

(36 citation statements)

References 20 publications

(29 reference statements)

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“…yellow circles) attached to hooves, limb joints, withers, or back-line contour. Video recordings of cows walking with markers are later analysed with software for kinematic variables (Flower et al, 2005;Aoki et al, 2006;Blackie et al, 2013). Another technique uses image preprocessing, in which video recordings are transformed into sequences of binary images to facilitate the detection of anatomical parts of cows (Song et al, 2008;Van Hertem et al, A c c e p t e d M a n u s c r i p t 2013; Viazzi et al, 2013).…”

Section: Automatic Locomotion Scoring Systemsmentioning

confidence: 99%

Manual and automatic locomotion scoring systems in dairy cows: A review

Schlageter-Tello

Bokkers

Koerkamp

et al. 2014

Preventive Veterinary Medicine

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Section: Automatic Locomotion Scoring Systemsmentioning

confidence: 99%

Manual and automatic locomotion scoring systems in dairy cows: A review

Schlageter-Tello

Bokkers

Koerkamp

et al. 2014

Preventive Veterinary Medicine

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“…This suggestion is in line with earlier studies, which showed that back curvature was one of the main indicators for locomotion scoring (Sprecher et al, 1997;Van Nuffel et al, 2009). Back curvature is also considered to be a reliable measure for lameness classification (Blackie et al, 2013;Hoffman et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Back curvature can be extracted from images by different feature variables, such as an inverse radius Viazzi et al, 2014), different curvature angles ) and a back posture measurement (BPM) . The strong relation between back arch and gait abnormality is proven (Sprecher et al, 1997;Blackie et al, 2013;Hoffman et al, 2014).…”

Section: Introductionmentioning

confidence: 96%

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

Hertem

Bahr

Tello

et al. 2016

animal

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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 videobased 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 Θ 2 , 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.

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“…Another group researcher reported that fully automated lameness detection system using load cells (Rajkondawar, U., et al [35], Pastell, M., et al [36], Liu, J., et al [37]). Another group researcher is also study about cattle lameness detection system using computer vision [38][39][40][41][42]. The system report lame cattle in need of treatment must be separated from the herd automatically.…”

Section: Health Observationmentioning

confidence: 99%

“…Sato, S., et al [49], developed bolus-type wireless sensor nodes which were inserted into the rumen using a special instrument via the cow's oral cavity. The ruminal pH was measured and stored every 15 minutes and this information was then transmitted wirelessly [40]. The sensor can be used continuously for a period of two to three months without replacing the battery, and can be recovered orally with special instruments.…”

Section: Health Observationmentioning

confidence: 99%

Futuristic Applications for Profitable Beef Production Systems

Göncü¹,

Anitaş²,

Gökçe³

2017

JESE-A

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Abstract:The growing demands for red meats of people also present opportunities for fattening as well as improved markets for the animals. Beef production is a highly profitable venture with return of premium to the farmer. The large scale beef farming must be meet economic criteria for sustainable production. It consists of buying healthy stock, feeding and fattening them to optimum carcass weight, and selling them at any time of the year. They are handled in such a way that maximum growth is achieved at the lowest possible cost. The lifespan of operation is shorter and the return of investment is relatively higher. In beef farms which has very high genetic value of breeding animals cannot be get the expected performance without the use of technology and automation systems. Beef herd management programs if can be used as effectively, farming will have many advantages for consumer, farmer and also animals. However, to obtain these advantages from this system, required to have knowledge of the functions and effective use of the functions. The large amount of data in the obtained on many issues related to animals, herd management and an individual unless used in decisions about animals, ensuring the heavy data flow, record keeping or assessment will not give the expected results. In this study, it has been aiming that the examined the technologic applications at beef farms for sustainable and profitable meat production.

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Associations between locomotion score and kinematic measures in dairy cows with varying hoof lesion types

Cited by 42 publications

References 20 publications

Manual and automatic locomotion scoring systems in dairy cows: A review

Manual and automatic locomotion scoring systems in dairy cows: A review

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

Futuristic Applications for Profitable Beef Production Systems

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