ArticleProceedings. Biological sciences2021
Do we automatically detect health- or general welfare-related issues? A framework.
Article in Proceedings. Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Agri-food data spaces: Highlighting the need for a farm-centered strategy.Data in brief · 2025Article
- Evaluation of candidate data-based welfare indicators for veal calves in Switzerland.Frontiers in veterinary science · 2024Article
- Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.Frontiers in veterinary science · 2024Article
- Accelerometer-based detection of African swine fever infection in wild boar.Proceedings. Biological sciences · 2023Article
- Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics.Animals : an open access journal from MDPI · 2023Article
- Can We Reliably Detect Respiratory Diseases through Precision Farming? A Systematic Review.Animals : an open access journal from MDPI · 2023Review
- Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project.Frontiers in veterinary science · 2023Article
- Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms.Animals : an open access journal from MDPI · 2022Review
- Automated identification of chicken distress vocalizations using deep learning models.Journal of the Royal Society, Interface · 2022Article
- Article
- Clinical Scores in Veterinary Medicine: What Are the Pitfalls of Score Construction, Reliability, and Validation? A General Methodological Approach Applied in Cattle.Animals : an open access journal from MDPI · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The early detection of health disorders is a central goal in livestock production. Thus, a great demand for technologies enabling the automated detection of such issues exists. However, despite decades of research, precision livestock farming (PLF) technologies with sufficient accuracy and ready for implementation on commercial farms are rare. A central factor impeding technological development is likely the use of non-specific indicators for various issues. On commercial farms, where animals are exposed to changing environmental conditions, where they undergo different internal states and, most importantly, where they can be challenged by more than one issue at a time, such an approach leads inevitably to errors. To improve the accuracy of PLF technologies, the presented framework proposes a categorization of the aim of detection of issues related to general welfare, disease and distress and defined disease. Each decision level provides a different degree of information and therefore requires indicators varying in specificity. Based on these considerations, it becomes apparent that while most technologies aim to detect a defined health issue, they facilitate only the identification of issues related to general welfare. To achieve detection of specific issues, new indicators such as rhythmicity patterns of behaviour or physiological processes should be examined.
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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.