Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques

Poursaberi, Ahmad; Bahr, Claudia; Pluk, Arno; Nuffel, Annelies Van; Berckmans, Daniël

doi:10.1016/j.compag.2010.07.004

Cited by 127 publications

(84 citation statements)

References 32 publications

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“…The objective measurement of gait patterns in research studies is mainly based on 2 methods: (1) kinematic (changes in the position of the body segments during a specified time) gait analysis (Flower et al, 2005), such as high-speed cinematography with the cow on a treadmill (Schmid et al, 2009), image-processing techniques (Poursaberi et al, 2010;Viazzi et al, 2013), or accelerometers (Pastell et al, 2009;Alsaaod et al, 2015); and (2) kinetic (force applied to the body) gait analysis using 1-or 3-dimensional ground reaction force systems (Rajkondawar et al, 2006;Walker et al, 2010;Thorup et al, 2014) or pressure-sensitive walkways (Van Nuffel et al, 2009;Maertens et al, 2011). One of the drawbacks of some of these methods is that they are expensive or difficult to apply in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Alsaaod

Luternauer

Hausegger

et al. 2017

Journal of Dairy Science

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Changes in gait characteristics are important indicators in assessing the health and welfare of cattle. The aim of this study was to detect unilateral hind limb lameness and foot pathologies in dairy cows using 2 high-frequency accelerometers (400 Hz). The extracted gait cycle variables included temporal events (kinematic outcome = gait cycle, stance phase, and swing phase duration) and several peaks (kinetic outcome = foot load, toe-off). The study consisted of 2 independent experiments. Experiment 1 was carried out to compare the pedogram variables between the lateral claw and respective metatarsus (MT; n = 12) in sound cows (numerical rating system <3, n = 12) and the differences of pedogram variables across limbs within cows between lame cows (numerical rating system ≥3, n = 5) and sound cows (n = 12) using pedogram data that were visually compared with the synchronized cinematographic data. Experiment 2 was carried out to determine the differences across limbs within cows between cows with foot lesions (n = 12) and without foot lesions (n = 12) using only pedogram data. A receiver operator characteristic analysis was used to determine the performance of selected pedogram variables at the cow level. The pedogram of the lateral claw of sound cows revealed similarities of temporal events (gait cycle duration, stance and swing phases) but higher peaks (toe-off and foot load) as compared with the pedogram of the respective MT. In both experiments, comparison of the values between groups showed significantly higher values in lame cows and cows with foot lesions for all gait cycle variables. The optimal cutoff value of the relative stance phase duration for identifying lame cows was 14.79% and for cows with foot lesions was 2.53% with (both 100% sensitivity and 100% specificity) in experiments 1 and 2, respectively. The use of accelerometers with a high sampling rate (400 Hz) at the level of the MT is a promising tool to indirectly measure the kinematic variables of the lateral claw and to detect unilateral hind limb lameness and hind limb pathologies in dairy cows and is highly accurate.

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Section: Introductionmentioning

confidence: 99%

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Alsaaod

Luternauer

Hausegger

et al. 2017

Journal of Dairy Science

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“…Collection of some of the physiological measurements require the animal to be restrained, compromising the ability to interpret changes as painful as opposed to stressful. Other pain assessment systems have been developed in response to this, and they rely on monitoring short-term behaviors such as vocalizations [96], escape attempts [97], lameness [94], or posture changes [57,98] where animals try to adopt a position that limits the pain experienced. Many of these indicators are not pain specific and can be seen in response to other affective states such as fear or stress, or even positive states such as joy.…”

Section: Assessing Pain In Farm Animalsmentioning

confidence: 99%

Why Pain Is Still a Welfare Issue for Farm Animals, and How Facial Expression Could Be the Answer

McLennan

2018

Agriculture

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Pain is a sensory and emotional experience that significantly affects animal welfare and has negative impacts on the economics of farming. Pain is often associated with common production diseases such as lameness and mastitis, as well as introduced to the animal through routine husbandry practices such as castration and tail docking. Farm animals are prey species which tend not to overtly express pain or weakness, making recognizing and evaluating pain incredibly difficult. Current methods of pain assessment do not provide information on what the animal is experiencing at that moment in time, only that its experience is having a long term negative impact on its behavior and biological functioning. Measures that provide reliable information about the animals' affective state in that moment are urgently required; facial expression as a pain assessment tool has this ability. Automation of the detection and analysis of facial expression is currently in development, providing further incentive to use these methods in animal welfare assessment.

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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%

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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Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques

Cited by 127 publications

References 32 publications

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Why Pain Is Still a Welfare Issue for Farm Animals, and How Facial Expression Could Be the Answer

Futuristic Applications for Profitable Beef Production Systems

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