ReviewFrontiers in veterinary science2021
The Use of Artificial Intelligence in Assessing Affective States in Livestock.
Review in Frontiers in veterinary science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Artificial intelligence-based dairy cattle behavior recognition for estrus detection via ensemble fusion of two camera views.PloS one · 2026Article
- When touch is stressful: acute endocrine and behavioral responses of domestic rabbits to unfamiliar human handling.Frontiers in veterinary science · 2026Article
- Automated analysis of feline posture using deep learning and geometric modeling.Frontiers in veterinary science · 2026Article
- Artificial Intelligence-Assisted Selection Strategies in Sheep: Linking Reproductive Traits with Behavioral Indicators.Animals : an open access journal from MDPI · 2025Review
- Integrating artificial intelligence into veterinary education: student perspectives.Frontiers in veterinary science · 2025Article
- Music and Emotions in Non-Human Animals from Biological and Comparative Perspectives.Animals : an open access journal from MDPI · 2024Review
- Human-computer interactions with farm animals-enhancing welfare through precision livestock farming and artificial intelligence.Frontiers in veterinary science · 2024Article
- ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.Journal of animal science · 2022Review
- Automated Tracking Systems for the Assessment of Farmed Poultry.Animals : an open access journal from MDPI · 2022Review
- Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming.Frontiers in veterinary science · 2021Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.