Publication Database

Evaluation of different sensor systems for classifying the behavior of dairy cows on pasture (2024)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Pichlbauer, Barbara (WE 1)

Jose Maria Chapa, Gonzalez

Bobal, Martin

Guse, Christian

Iwersen, Michael (WE 19)

Drillich, Marc (WE 18)

Quelle

Sensors

Bandzählung: 24

Heftzählung: 23

Seiten: 7739

ISSN: 1424-8220

Sprache

Englisch

Verweise

URL (Volltext): https://www.mdpi.com/1424-8220/24/23/7739

DOI: 10.3390/s24237739

Pubmed: 39686278

Kontakt

Nutztierklinik

Königsweg 65
14163 Berlin
+49 30 838 62261
klauentierklinik@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.

Keywords: evaluation, sensor technology, monitoring, behavior, cattle, grazing