Development and validation of a fully automated 2-dimensional imaging system generating body condition scores for dairy cows using machine learning

Siachos, N.; Lennox, M.; Anagnostopoulos, A.; Griffiths, B.E.; Neary, J.M.; Smith, R.F.; Oikonomou, G.

doi:10.3168/jds.2023-23894

Cited by 1 publication

(1 citation statement)

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“…For example, concerns around cost could be overcome by undertaking research to improve the effectiveness and efficiency of technologies. Efficiency could be improved by using machine learning to process large amounts of data to provide highly detailed information about the behaviours and states of monitored animals, in some cases using ambient sensors rather than instrumenting individual animals (Hansen et al 2018 ; Anagnostopoulos et al 2023 ; Siachos et al 2023 ). This means that once the technology is in place, monitoring an increasing number of animals does not necessarily increase costs exponentially.…”

Section: Resultsmentioning

confidence: 99%

Human-animal interactions and machine-animal interactions in animals under human care: A summary of stakeholder and researcher perceptions and future directions

Williams,

Sadler,

Rutter

et al. 2024

Anim. welf.

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Animals under human care are exposed to a potentially large range of both familiar and unfamiliar humans. Human-animal interactions vary across settings, and individuals, with the nature of the interaction being affected by a suite of different intrinsic and extrinsic factors. These interactions can be described as positive, negative or neutral. Across some industries, there has been a move towards the development of technologies to support or replace human interactions with animals. Whilst this has many benefits, there can also be challenges associated with increased technology use. A day-long Animal Welfare Research Network workshop was hosted at Harper Adams University, UK, with the aim of bringing together stakeholders and researchers (n = 38) from the companion, farm and zoo animal fields, to discuss benefits, challenges and limitations of human-animal interactions and machine-animal interactions for animals under human care and create a list of future research priorities. The workshop consisted of four talks from experts within these areas, followed by break-out room discussions. This work is the outcome of that workshop. The key recommendations are that approaches to advancing the scientific discipline of machine-animal interactions in animals under human care should focus on: (1) interdisciplinary collaboration; (2) development of validated methods; (3) incorporation of an animal-centred perspective; (4) a focus on promotion of positive animal welfare states (not just avoidance of negative states); and (5) an exploration of ways that machines can support a reduction in the exposure of animals to negative human-animal interactions to reduce negative, and increase positive, experiences for animals.

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Section: Resultsmentioning

confidence: 99%