A System for Identifying Lameness in Dairy Cattle

Rajkondawar, P.G.; Tasch, U.; Lefcourt, Alan M.; Erez, B.; Dyer, R.M.; Varner, M.A.

doi:10.13031/2013.7707

Cited by 57 publications

(36 citation statements)

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“…The increasing size of dairy herds has led to interest in more objective and automated methods of recording the gait of cows and detecting lameness. Force plate systems have been applied to measure ground reaction forces when cows are walking (Rajkondawar et al . 2002a,b) or the weight cows place on each leg while standing (Neveux et al .…”

Section: Introductionmentioning

confidence: 99%

Use of three-dimensional acceleration sensing to assess dairy cow gait and the effects of hoof trimming

Tanida

Koba

Rushen

et al. 2011

Animal Science Journal

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The aim of this study was to assess the use of three-dimensional acceleration sensing to describe the gait of dairy cows and the effect of hoof trimming. In Trial 1, a three-dimensional acceleration sensor was attached to the carpal region of a front leg of six Holstein cows who were then walked for 9 m. Results showed that measures of acceleration could clearly show the presence of steps, which were divided into an acceleration phase, which involved the hoof descending to and ascending from the ground, and a non-acceleration phase, which included the swing phase. Lissajous figures could succinctly depict the degree of acceleration for each cow and showed that one cow who was lame walked with greater vertical and lateral acceleration than the remaining cows. In Trial 2, 17 loose-housed Holstein cows were walked for 20 m during the month before, once during the month after and once 2 months after hoof trimming with the sensor attached at the end of their thoracic vertebrae to measure acceleration of the whole body. To relate the acceleration of the body to movements of the legs, image analysis was used to the range of vertical movement of four major skeletal joints, using visual markers attached to them. Hoof trimming significantly (P < 0.005) decreased both the range of vertical movement of the joints of front and hind legs and the variance of lateral and forward acceleration at the end of the thoracic vertebrae, suggesting improved gait pattern smoothness. Acceleration sensing was able to characterize variation among gait patterns of the cows and could reproduce the pattern shown by image analysis. This study demonstrates that three-dimensional acceleration sensing of either the carpal region of a front leg or the end of the thoracic vertebrae is useful to detect walking irregularities and evaluate the effectiveness of hoof trimming on walking ability of cattle.

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Section: Introductionmentioning

confidence: 99%

Use of three-dimensional acceleration sensing to assess dairy cow gait and the effects of hoof trimming

Tanida

Koba

Rushen

et al. 2011

Animal Science Journal

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“…Sprecher and others (1997) described a locomotion scoring system that is used worldwide to detect lameness. More recently, technological approaches have been developed to detect lameness more objectively (Rajkondawar and others 2002a, b, Tasch and Rajkondawar 2004, Flower and others 2005, Flower and Weary 2006). …”

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confidence: 99%

Use of force sensors to detect and analyse lameness in dairy cows

2008

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Force sensors were used to detect lameness in dairy cows in two trials. In the first trial, leg weights were recorded during approximately 12,000 milkings with balances built into the floor of the milking robot. Cows that put less weight on one leg or kicked frequently during milking were checked first with a locomotion scoring system and then with a clinical inspection. A locomotion score of more than 2 was considered lame, and these cows' hooves were examined at hoof trimming to determine the cause and to identify any hoof lesions. In the second trial 315 locomotion scores were recorded and compared with force sensor data. The force sensors proved to be a good method for recognising lameness. Computer curves drawn from force sensor data helped to find differences between leg weights, thus indicating lameness and its duration. Sole ulcers and white line disease were identified more quickly by force sensors than by locomotion scoring, but joint problems were more easily detected by locomotion scoring.

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“…All crossbred cows, lactating, dry, pregnant and non-pregnant were maintained under loose housing and group management system (not more than 40 cows in a single group) in large, open and brick paved paddock adjacent to Tandem milking parlour. The weight distribution of cows were obtained using a load cell platform weighing system with real time logging (Electronic Static Weigh-pad) positioned in the floor of alley of milking parlor, somewhat similar to the pressure plate or four-balance system (Rajkondawar et al, (2002a), Pastell and Kujala (2007), Rushen et al, (2007), and Almeida et al, (2007). Each cow was given sufficient time to adjust herself on the platform.…”

Section: A Materials and Weight Measurementmentioning

confidence: 99%

“…gait scoring (visual inspection) [15,23] lacks sensitivity, specificity [9,8,11] and accuracy [7]. In recent years, some automated lameness assessment methods/techniques has been developed which overcome many problems associated with gait scoring technique like-Force Plate System [19], Kinematics Modeling [11], Electromyography [14], Accelerometer [17], Vision Based Trackway Analysis [22] and Shape Analysis of cow with Image Processing Technique [18] etc. These techniques now a day are gaining popularity in large commercial dairy farms to detect lame cow.…”

Section: Introductionmentioning

confidence: 99%

Detection of lameness of cow based on body weight using artificial neural network

Gupta

Lathwal²,

Mohanty

et al. 2014

2014 International Conference on Computing for Sustainable Global Development (INDIACom)

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Present study describes the application of machine learning algorithms to detect the health status of Karan Fries crossbred cow, whether cow is lame or healthy. A total of 589 Karan Fries crossbred cows data from the herd maintained at National Dairy Research Institute (NDRI), Karnal were recorded for the health status (lame or healthy) as target variable and other non-genetic variables such as percent of body weight distributed to individual legs (using load cell platform), parity (1to10), status of pregnancy (non-pregnant, less than 90days, 91-180days and more than 181days pregnant), status of lactation (less than 60days, 61-120days, more than 121days and dry), and daily milk yield were used as input variables. These variables were used for the development of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks (NNs). Simulation of network were carried out using varying data partition strategies for training and validation data sets (60:40, 70:30 and 80:20), number of nodes in hidden layers and different optimization algorithms. It was found that to predict the heath status of cows RBF neural architect with Leven-burg Marquardt (LM) optimization algorithm, gave the best performance in comparison to MLP with highest classification accuracy rate (83.19%) at 80:20 data partition strategy.

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A System for Identifying Lameness in Dairy Cattle

Cited by 57 publications

References 0 publications

Use of three-dimensional acceleration sensing to assess dairy cow gait and the effects of hoof trimming

Use of three-dimensional acceleration sensing to assess dairy cow gait and the effects of hoof trimming

Use of force sensors to detect and analyse lameness in dairy cows

Detection of lameness of cow based on body weight using artificial neural network

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