ArticleSensors (Basel, Switzerland)2024
Evaluation of Different Sensor Systems for Classifying the Behavior of Dairy Cows on Pasture.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Circadian Thresholds for Stress Detection in Dairy Cattle.Sensors (Basel, Switzerland) · 2025Article
- FSCA-YOLO: An Enhanced YOLO-Based Model for Multi-Target Dairy Cow Behavior Recognition.Animals : an open access journal from MDPI · 2025Article
- DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- Welfare indicators in cattle farming in the face of heat stress: a review in climate change scenarios.Frontiers in veterinary science · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Monitoring animal behavior using sensor technologies requires prior testing under varying conditions because behaviors can differ significantly, such as between grazing and confined cows. This study aimed to validate several sensor systems for classifying rumination and lying behaviors in cows on pasture under different environmental conditions, compare the sensors' performance at different time resolutions, and evaluate a correction algorithm for rumination data. Ten Simmental dairy cows were monitored on pasture, each simultaneously equipped with an ear-tag accelerometer (ET), two different leg-mounted accelerometers (LMs), and a noseband sensor (NB). Indirect visual observations using drone-recorded video footage served as the gold standard for validation. The concordance correlation coefficient (CCC) for rumination time was very high for both the ET and NB (0.91-0.96) at a 10 min time resolution. Applying the correction algorithm to 1 min data improved the CCC for the NB from 0.68 to 0.89. For lying time, the CCC was moderate for the ET (0.55) but nearly perfect for both LMs (0.99). In conclusion, both sensors evaluated for classifying rumination are suitable for cows on pasture. We recommend using a correction algorithm for 1 min NB data. For the measurement of lying time, the LMs significantly outperformed the ET.
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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.