Validation of accelerometers to automatically record postures and number of steps in growing lambs

Högberg, Niclas; Höglund, Johan; Carlsson, Annelie; Saint-Jeveint, Marie; Lidfors, Lena

doi:10.1016/j.applanim.2020.105014

Cited by 16 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accelerometers embedded in wearable devices attached to livestock allow data to be collected corresponding to an animal's 3-dimensional movement patterns [82][83][84]. Analyses of these data can yield valuable insights into the behaviour, health, and welfare of livestock [85].…”

Section: Accelerometers -Body Movementmentioning

confidence: 99%

Affective State Recognition in Livestock – Artificial Intelligence Approaches

Neethirajan¹

2022

Preprint

View full text Add to dashboard Cite

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in the animal welfare research, the animal affective computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in nascent stage. By capitalizing on the immense potential of biometric sensors, the artificial intelligence enabled big data methods substantially offers advancement of animal welfare standards and meet the urgent need of caretakers to respond effectively to maintain the wellbeing of their animals. Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are no scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time consuming, interrupt farming processes and involve subjective judgments. Biometric sensors data enabled by Artificial Intelligence are an emerging smart solution to unobtrusively monitoring livestock, but their potential for quantifying affective states and groundbreaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and be-havior in real time are a near-term possibility.

show abstract

Section: Accelerometers -Body Movementmentioning

confidence: 99%

Affective State Recognition in Livestock – Artificial Intelligence Approaches

Neethirajan¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…On-animal monitoring requires livestock to be fitted with smart technology that can monitor the animals changing state. These changes include body movement, changing location and physiological parameters such as temperature or heart rate (Kour et al, 2018;Edwards et al, 2020;Högberg et al, 2020;Islam et al, 2020). The sensor devices log the data locally and then transfer it via a range of different communication technologies.…”

Section: Technology Developments Required To Deliver Fenceless Livestock Farmingmentioning

confidence: 99%

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

Swain

Charters

2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

The development and application of the fence was one of the earliest forms of agricultural technology in action. Managing the supply of animal protein required hunter gatherer communities to be able to domesticate and contain wild animals. Over the ages the fence has become ingrained in the very fabric of society and created a culture of control and ownership. Garett Hardin's article titled “The Tragedy of the Commons” suggested that shared land, typified by access to a fenceless common resource, was doomed to failure due to a human instinct for mistrust and exploitation. Perhaps the fence has created an ingrained societal cultural response. While natural ecosystems do have physical boundaries, these are based on natural environmental zones. Landscapes are more porous and resilience is built up through animal's being able to respond to dynamic changes. This paper explores the opportunity for remote monitoring technologies to create open fenceless landscapes and how this might be integrated into the growing need for humans to access animal protein.

show abstract

“…By combining GPS data and 3-axis accelerometry data González et al (27) were able to accurately detect foraging and travelling behaviour in grazing cattle. Högberg et al (28) measured the accuracy of two commercial sensors (CowScout(GEA Farm Technologies)) and the IceTag® (IceRobotics Ltd.) mounted on dairy cows. Both devices use accelerometers to determine lying, standing and walking.…”

Section: Significance Statementmentioning

confidence: 99%

Early prediction of declining health in small ruminants with accelerometers and machine learning

Montout

Bhamber

Lange

et al. 2020

Preprint

View full text Add to dashboard Cite

Accurate assessment of the health status of individual animals is a key step in timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect levels of anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals over the Haemonchus season. Here, we show that accelerometers can measure individual activity in extensively grazing small ruminants subject to natural Haemonchus contortus worm infection in southern Africa over long time-scales, and when combined with machine learning, can predict the smallest pre-clinical increases in FAMACHA score as well as those individuals that respond to treatment, all with high precision (>95%). We demonstrate that these classifiers remain robust over time, and remarkably, generalise without retraining across goats and sheep in different regions and types of farming enterprise. Interpretation of the trained classifiers reveal that as the effect of haemonchosis increases, both sheep and goats exhibit a similar reduction in the fine-grained variation of their activity levels. Our study thus reveals common behavioural patterns across small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and hence diminish pressures on anthelmintic resistance under conditions of both commercial and resource-poor communal farming.

show abstract

Validation of accelerometers to automatically record postures and number of steps in growing lambs

Cited by 16 publications

References 28 publications

Affective State Recognition in Livestock – Artificial Intelligence Approaches

Affective State Recognition in Livestock – Artificial Intelligence Approaches

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

Early prediction of declining health in small ruminants with accelerometers and machine learning

Contact Info

Product

Resources

About