Potential for Estimation of Body Condition Scores in Dairy Cattle from Digital Images

Bewley, J.M.; Peacock, A.M.; Lewis, Owen M.; Boyce, Robert E; Roberts, David J.; Coffey, M.P.; Kenyon, S.J.; Schutz, M.M.

doi:10.3168/jds.2007-0836

Cited by 116 publications

(117 citation statements)

References 17 publications

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“…This has the potential benefit that cameras are inherently cheaper and simpler to maintain than mechanical weigh cells. Bewley et al (2008) were able to show a good correlation between BCS and digitally processed static images. Anglart (2014) was able to show a good correlation between automatically captured digital images and weight on Swedish Red cows but had difficulties with black and white pigmented cows.…”

Section: Metabolic Disorders: Rumen Sensingmentioning

confidence: 77%

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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Section: Metabolic Disorders: Rumen Sensingmentioning

confidence: 77%

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

Mottram

2016

Animal

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“…In the Table 1 ii. Mostly, there are two types of models used: regression analysis models (as in) [10][11][12][13][14][15][16] and algorithms that measure cow's body angularity (as in) [17][18][19] according to the hypothesis that the body shape of a fatter cow is rounder than that of a thin cow. Moreover, three automation levels are described.…”

Section: Discussionmentioning

confidence: 99%

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

Alvarez

Arroqui

Mangudo

et al. 2017

JDVAR

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BCS is a method to estimate body fat stores and accumulated energy balance of cows. This value influences productivity, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve a better animal response. In practice, this task is performed by expert scorers mainly visually, and could vary between scorers and be time-consuming. For this reason, several studies have tried to automate BCS by applying image analysis and machine learning techniques. An overview of selected studies is provided in this mini review. 74% within 0.25, 91% within 0.5 2D, two dimensional; 3D, three-dimensional; ToF, time-of-flight; SRB, swedish red breed; GLM, generalized linear model; US-BCS, united state body condition score; UK-BCS, united kingdom body condition score; R, correlation coefficient; R 2 , coefficient of determination; LOOCV, leave one out cross validation; RMSE, root mean square error ConclusionThe literature attempts to automate BCS assessment look promising as a tool for supporting cattle decision-making, in a context where ICT technology is becoming more efficient, productive, and cheaper. Acceptable accuracy within the range of human error has been reported, with room for improvement as more effective computing processing methods became available. AcknowledgementsNone. Conflict of interestAuthor declares that there is no conflict of interest.

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“…As already mentioned, it could be used to predict carcass cut yields. In live animals, a trait of special interest is body condition score (Bewley et al, 2008;Halachmi et al, 2008) because of its association with productivity, health, longevity and welfare (Roche et al, 2009). Imaging can accurately detect and measure angularity, and intensive work in this field is currently being carried out.…”

Section: Introductionmentioning

confidence: 99%

New phenotypes for new breeding goals in dairy cattle

Boichard

Brochard

2012

Animal

108

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Cattle production faces new challenges regarding sustainability with its three pillars -economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases.(2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

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Potential for Estimation of Body Condition Scores in Dairy Cattle from Digital Images

Cited by 116 publications

References 17 publications

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

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

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

New phenotypes for new breeding goals in dairy cattle

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