ReviewAnimals : an open access journal from MDPI2022
Affective State Recognition in Livestock-Artificial Intelligence Approaches.
Review in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 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
15 citing papers in PubMed, 57 citations in OpenAlex.
- Developing Intelligent Models to Detect and Classify Cattle Behavior on Pasture.Animals : an open access journal from MDPI · 2026Article
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
- STELLAR-CB: Synthetic Temporal LSTM for Livestock Activity Recognition-Cow Behaviour.Veterinary medicine and science · 2026Article
- Qualitative behavioural assessment shows individual variation suggestive of affective state changes in early lactation dairy cows.Frontiers in veterinary science · 2026Article
- Relevance of state-behaviour feedbacks for animal welfare.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Measuring minds: Understanding the mental states of dairy cattle in different management conditions.JDS communications · 2025Article
- AI and Data Analytics in the Dairy Farms: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- A Complete Pipeline to Extract Temperature from Thermal Images of Pigs.Sensors (Basel, Switzerland) · 2025Article
- AI for One Welfare: the role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools.Frontiers in veterinary science · 2025Article
- Enhancing equine welfare: a qualitative study on the impact of RAiSE (Recognizing Affective States in Equine) as an educational tool.Translational animal science · 2025Article
- Faces of time: a historical overview of rapid innovations in coding animal facial signals.Frontiers in veterinary science · 2025Review
- Article
- Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.Sensors (Basel, Switzerland) · 2023Review
- Correction: Neethirajan, S. Affective State Recognition in Livestock-Artificial Intelligence Approaches.Animals : an open access journal from MDPI · 2022Article
- Review
Corrections and comments
- Erratum issued
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 not 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 sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking 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 behaviour in real time are a near-term possibility.
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.