A Probabilistic Neural Network Model for Lameness Detection

Pastell, Matti; Kujala, Minna

doi:10.3168/jds.2006-267

Cited by 98 publications

(107 citation statements)

References 27 publications

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“…Due to the reduced opportunities for handling cows in AMS, there is interest in finding automated means of detecting illness, including lameness. Walking platforms, which measure the force that cows exert when walking or standing, may be one option (e.g., Rajkondawar et al 2002;Rushen et al 2007) and could be incorporated into an AMS (Pastell et al 2007). However, AMS gather and maintain considerable data on the pattern of cows' visits to the milking unit and our results suggest that the visiting frequency (either a low frequency or a reduction from the usual number of visits) could be used as a tool for helping to identify lame cows.…”

Section: Discussionmentioning

confidence: 99%

Effect of lameness on dairy cows’ visits to automatic milking systems

Borderas¹,

Fournier²,

Rushen³

et al. 2008

Can. J. Anim. Sci.

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. 2008. Effect of lameness on dairy cows' visits to automatic milking systems. Can. J. Anim. Sci. 88: 1Á8. Lameness is a major welfare problem for dairy cows and has important economic consequences. On-farm detection of lameness is difficult, and automated methods may be useful for early diagnoses. Lameness may reduce the efficiency of automated milking systems (AMS) if lame cows are less willing to visit the automatic milking unit voluntarily and poor attendance at milking units may help detect lameness. To determine whether a low frequency of visits in an AMS could serve as an indicator of lameness, data on the frequency of visits of 578 cows in 12 AMS on eight farms were collected. From each AMS, 22 cows (from a mean of 54 cows per AMS), were classified as either the 11 highest visitors or the 11 lowest visitors based on the total number of visits to the milking unit. These selected cows (n 0256) were videotaped while walking in a standard test area and their gait scored on a 5-point scale (1 0sound 5 0severely lame). Intra-and inter-observer reliability values between and within observers were high for gait scoring. Significant differences in gait scores between the two groups of cows (P B0.05) were found in 9 out of 12 AMS: high-visiting cows had better gait scores than low-visiting cows. Four percent of high visitors were classified as slightly lame and 32% of low visitors were classified as either slightly or severely lame. The overall numerical rating score was the most effective in discriminating between high and low visitors, and scoring each individual component of gait did not greatly improve discrimination between the two groups of cows. The frequency that dairy cows visit an AMS is related to their locomotory ability, and data from the AMS may help in the early detection of lameness.Key words: Cattle, lameness, automatic milking systems, behaviour, gait scoring Borderas, T. F., Fournier, A., Rushen, J. et de Passille´, A. M. B. 2008. Effect of lameness on dairy cows' visits to automatic milking systems. Can. J. Anim. Sci. 88: 1Á8. Les boiteries sont un proble`me de bien-eˆtre majeur pour les vaches laitie`res qui a des conse´quences e´conomiques importantes. Il est reconnu que la de´tection des boiteries est difficile a`faire pour les producteurs. Des me´thodes automatise´es seraient donc utiles pour assister au de´pistage haˆtif des vaches boiteuses. L'efficacite´des syste`mes de traite automatise´s (AMS) pourrait eˆtre re´duite si les vaches boiteuses sont moins dispose´es av isiter volontairement l'unite´de traite automatique (AMU). Ainsi, une fre´quence re´duite des visites aux unite´s de traite pourrait aider a`de´tecter les boiteries. Pour de´terminer si une basse fre´quence des visites du AMS pourrait servir a`de´pister les vaches boiteuses, les donne´es sur la fre´quence des visites de 578 vaches traites par 12 AMU sur huit fermes ont e´teŕ ecueillies. En utilisant le nombre total de visites au AMS, on a choisi, pour chaque AMS, les 11 plus hautes visiteuses et les 11 plus basse visiteuses ...

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Section: Discussionmentioning

confidence: 99%

Effect of lameness on dairy cows’ visits to automatic milking systems

Borderas¹,

Fournier²,

Rushen³

et al. 2008

Can. J. Anim. Sci.

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show abstract

“…When performed by trained veterinarians, visual locomotion scoring performed better than Stepmetrix in detecting cows with painful lesions. Pastell and Kujala (2007) used load cells in a robotic milker with 37 cow scores to measure leg load during milking and classified 96.2% correctly as mobility score (0,1,2) against lame (mobility score 3 or above). When a cow's mobility PLF dairy score increases the cows back arches and Viazzi et al (2013) compared two imaging methods to measure the degree of arching.…”

Section: Mobility and Lameness Measurementmentioning

confidence: 99%

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Mottram

2016

Animal

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Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.Keywords: dairy cow, sensors, fertility, precision livestock farming, automation ImplicationsSince the introduction of robotic milking technologies to measure important parameters of cow health and fertility have moved from scientific concepts towards robust techniques routinely used on farms. There is still potential for developing new approaches but the main focus is on applying modelling to data sources that are already available such as tri-axial cow collars, in-line milk sensing and rumen telemetry. IntroductionIn the past century dairy farms have become larger, milk yields have risen, quality requirements have risen, labour has become less available and automated systems for milking and other farm tasks have become common all of these factors have created a need for automated monitoring of health and fertility. The first systems for recording milk yield electronically and allocating feed date back to the 1970s and automatic oestrus behaviour systems from the 1980s. As electronics have become cheaper and new sensing capabilities developed a variety of engineering options were reviewed for all animals by Frost et al. (1997). Mottram (1997) reviewed potential target disease conditions in dairy cows and identified parturition, mastitis and failures in nutrition as the most significant causes of loss. It was suggested that sensing should focus on the most directly measurable indicator of a condition such as rumen pH rather than proxy measures. Since then major developments have taken plac...

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“…We have generally found that derived measures (stride overlap, time in triple support and speed) are more useful in detecting differences than basic kinematic measures like stride duration . Pastell and Kujala (2007) validated measures of force from cows standing in a robotic milker, modelling the data into a probabilistic neural network, against hoof lesions and gait scores, and found they could positively detect 100% lame cows with this technology. We suspect that sophisticated methods of data handling and analysing, like those reported by Pastell and Kujala (2007), will allow for better integration of objective data, and more powerful predictive models for detecting cows with hoof and leg pathologies.…”

Section: Validity Of Objective Measuresmentioning

confidence: 99%

Gait assessment in dairy cattle

Flower

Weary

2009

Animal

111

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Lameness is one of the most important dairy cow welfare issues and has inspired a growing body of literature on gait assessment. Validation studies have shown that several methods of gait assessment are able to successfully distinguish cows with and without painful pathologies. While subjective methods provide an immediate, on-site assessment and require no technical equipment, they show variation in observer reliability. On the other hand, objective methods of gait assessment provide accurate and reliable data, but typically require sophisticated technology, limiting their use on farms. In this critical review, we evaluate gait assessment methods, discuss the reliability and validity of measures used to date, and point to areas where new research is needed. We show how gait can be affected by hoof and leg pathologies, treatment of these ailments and the pain associated with lameness. We also discuss how cow (e.g. conformation, size and udder fill) and environmental features (e.g. flooring) contribute to variation in the way cows walk. An understanding of all these factors is important to avoid misclassifying of cows and confounding comparisons between herds.

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A Probabilistic Neural Network Model for Lameness Detection

Cited by 98 publications

References 27 publications

Effect of lameness on dairy cows’ visits to automatic milking systems

Effect of lameness on dairy cows’ visits to automatic milking systems

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Gait assessment in dairy cattle

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