ArticleAnimals : an open access journal from MDPI2019
Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle.
Article in Animals : an open access journal from MDPI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Dynamic Tracking of Respiratory Rate and Quantitative Analysis of Heat Stress Response of Caged Broilers Based on Infrared Thermal Imaging Video Amplification Technology.Animals : an open access journal from MDPI · 2026Article
- Reliability and precision of thermal imaging measurements to study animal behaviour and welfare.PeerJ · 2026Article
- Measuring minds: Understanding the mental states of dairy cattle in different management conditions.JDS communications · 2025Article
- Simultaneous, Non-Contact and Motion-Based Monitoring of Respiratory Rate in Sheep Under Experimental Condition Using Visible and Near-Infrared Videos.Animals : an open access journal from MDPI · 2024Article
- A feasibility study on predicting cow calving time over 40 h in advance using heart rate and financial technical indicators.Scientific reports · 2024Article
- Predicting respiration rate in unrestrained dairy cows using image analysis and fast Fourier transform.JDS communications · 2024Article
- Meat quality and safety issues during high temperatures and cutting-edge technologies to mitigate the scenario.Journal of animal science and technology · 2024Review
- Review
- Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.Animals : an open access journal from MDPI · 2023Article
- How should the respiration rate be counted in cattle?Veterinary research communications · 2022Article
- Livestock Identification Using Deep Learning for Traceability.Sensors (Basel, Switzerland) · 2022Article
- Technical note: a nose ring sensor system to monitor dairy cow cardiovascular and respiratory metrics.Journal of animal science · 2022Article
- Measuring haemolysis in cattle serum by direct UV-VIS and RGB digital image-based methods.Scientific reports · 2022Article
- Evaluation of the Best Region for Measuring Eye Temperature in Dairy Cows Exposed to Heat Stress.Frontiers in veterinary science · 2022Article
- Thermography for disease detection in livestock: A scoping review.Frontiers in veterinary science · 2022Article
- Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence.Sensors (Basel, Switzerland) · 2021Article
- Heart rate as a measure of emotional arousal in evolutionary biology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review.Animals : an open access journal from MDPI · 2021Review
- Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig.Animals : an open access journal from MDPI · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Precision livestock farming has emerged with the aim of providing detailed information to detect and reduce problems related to animal management. This study aimed to develop and validate computer vision techniques to track required features of cattle face and to remotely assess eye temperature, ear-base temperature, respiration rate, and heart rate in cattle. Ten dairy cows were recorded during six handling procedures across two consecutive days using thermal infrared cameras and RGB (red, green, blue) video cameras. Simultaneously, core body temperature, respiration rate and heart rate were measured using more conventional 'invasive' methods to be compared with the data obtained with the proposed algorithms. The feature tracking algorithm, developed to improve image processing, showed an accuracy between 92% and 95% when tracking different areas of the face of cows. The results of this study also show correlation coefficients up to 0.99 between temperature measures obtained invasively and those obtained remotely, with the highest values achieved when the analysis was performed within individual cows. In the case of respiration rate, a positive correlation (r = 0.87) was found between visual observations and the analysis of non-radiometric infrared videos. Low to high correlation coefficients were found between the heart rates (0.09-0.99) obtained from attached monitors and from the proposed method. Furthermore, camera location and the area analysed appear to have a relevant impact on the performance of the proposed techniques. This study shows positive outcomes from the proposed computer vision techniques when measuring physiological parameters. Further research is needed to automate and improve these techniques to measure physiological changes in farm animals considering their individual characteristics.
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