Treffer: Deep learning approach for classifying grazing behavior in yearling horses using triaxial accelerometer data: A pilot study.

Title:
Deep learning approach for classifying grazing behavior in yearling horses using triaxial accelerometer data: A pilot study.
Authors:
Kamiya U; School of Agriculture and Animal Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan., Kakiuchi K; School of Agriculture and Animal Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan., Kawamura K; Department of Agro-environmental Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan. Electronic address: kamuken@obihiro.ac.jp., Ueda K; Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan., Kawai M; Shizunai Livestock Farm, Field Science Center for Northern Biosphere, Hokkaido University, Shin-Hidaka, Hokkaido 056-0141, Japan., Matsui A; Equine Science Division, Hidaka Training and Research Center, Japan Racing Association, Hokkaido, 057-0171, Japan., Negishi N; Equine Science Division, Hidaka Training and Research Center, Japan Racing Association, Hokkaido, 057-0171, Japan.
Source:
Journal of equine veterinary science [J Equine Vet Sci] 2025 Dec; Vol. 155, pp. 105706. Date of Electronic Publication: 2025 Oct 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: United States NLM ID: 8216840 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 0737-0806 (Print) Linking ISSN: 07370806 NLM ISO Abbreviation: J Equine Vet Sci Subsets: MEDLINE
Imprint Name(s):
Publication: 2003- : New York : Elsevier
Original Publication: [Wildomar, CA : William E. Jones, c1981-
MeSH Terms:
Accelerometry*/veterinary , Accelerometry*/methods , Accelerometry*/instrumentation , Deep Learning* , Feeding Behavior* , Behavior, Animal*, Animals ; Horses/physiology ; Pilot Projects
Contributed Indexing:
Keywords: Accelerometry; Deep learning; Equine behavior; Pasture management; Precision livestock farming
Entry Date(s):
Date Created: 20251003 Date Completed: 20251205 Latest Revision: 20251205
Update Code:
20251206
DOI:
10.1016/j.jevs.2025.105706
PMID:
41043567
Database:
MEDLINE

Weitere Informationen

Background: Accurate monitoring of grazing behavior in horses is essential for pasture management and welfare evaluation; however, conventional observation methods are labor-intensive and lack temporal resolution.
Aims/objectives: This pilot study aimed to develop and validate a deep learning model using jaw-mounted accelerometer data to classify grazing and non-grazing behaviors in yearling horses under various pasture conditions.
Methods: Four yearling Thoroughbred horses were equipped with triaxle accelerometers mounted under their jaws. Data were recorded at 10 Hz (100 ms) during a 19 h free-grazing period in a 4.0 ha paddock. A total of 230,286 data points were annotated as grazing (G) or non-grazing (NG) using synchronized video observation. Three deep learning models-one-dimensional convolutional neural network (CNN), long short-term memory (LSTM), and combined CNN+LSTM-were trained and evaluated under varying sampling rates (100-10,000 ms) and time windows (5-60 s). Model performance was assessed using accuracy, F1 score, precision, recall, and area under the curve (AUC).
Results: The CNN+LSTM model demonstrated the highest classification performance with a test accuracy of 98.0 % and an AUC of 1.00. F1 scores were 0.99 for G and 0.97 for NG behavior. Across the full observational period, the proportion of grazing behavior was 58.3 % (±2.1 %). Spatial analysis revealed that grazing was concentrated along paddock peripheries, whereas non-grazing was more frequent in central zones.
Conclusion: A deep learning framework that combines CNN and LSTM can accurately classify grazing behavior in horses using jaw-mounted accelerometers. This non-invasive, high-resolution method offers a promising tool for automated behavioral monitoring in pasture-based systems.
(Copyright © 2025. Published by Elsevier Inc.)

Declaration of competing interest None of the authors has any financial or personal relationships that could inappropriately influence or bias the content of this paper.