Document Type
Article
Journal/Book Title/Conference
Animals
Author ORCID Identifier
Bashiri Iddy Muzzo https://orcid.org/0000-0001-5645-4923
Kelvyn Bladen https://orcid.org/0000-0002-2028-5504
Shelemia Nyamuryekung’e https://orcid.org/0000-0001-9323-0208
Juan J. Villalba https://orcid.org/0000-0001-8868-8468
Volume
15
Issue
7
Publisher
MDPI AG
Publication Date
3-22-2025
Journal Article Version
Version of Record
First Page
1
Last Page
20
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
This study classified cows’ foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. Models included Perceptron, Logistic Regression, Support Vector Machine, KNearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), posture states (standing up (SU) vs. lying down (LD)), and posture combinations with rumination and resting behaviors (RU_SU, RU_LD, RE_SU, and RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF outperformed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and behaviors-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: speed and Actindex were crucial for GR and W when increasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in activity states classification while CV in foraging behaviors and by posture classification. These results emphasize CV in RF’s reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement data to monitor cattle behavior accurately.
Recommended Citation
Muzzo, B.I.; Bladen, K.; Perea, A.; Nyamuryekung’e, S.; Villalba, J.J. Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior. Animals 2025, 15, 913. https://doi.org/10.3390/ani15070913