PMID- 35565487
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220517
IS  - 2076-2615 (Print)
IS  - 2076-2615 (Electronic)
IS  - 2076-2615 (Linking)
VI  - 12
IP  - 9
DP  - 2022 Apr 20
TI  - Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements 
      Based on Machine Learning Methods.
LID - 10.3390/ani12091060 [doi]
LID - 1060
AB  - The behavior of livestock on farms is the primary representation of animal 
      welfare, health conditions, and social interactions to determine whether they are 
      healthy or not. The objective of this study was to propose a framework based on 
      inertial measurement unit (IMU) data from 10 dairy cows to classify unitary 
      behaviors such as feeding, standing, lying, ruminating-standing, 
      ruminating-lying, and walking, and identify movements during unitary behaviors. 
      Classification performance was investigated for three machine learning algorithms 
      (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm 
      (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, 
      rolling biting, and chewing in the correctly classified feeding segments were 
      analyzed by the magnitude of the acceleration. The results revealed that the 
      XGBoost had the highest performance in the 60 s time window with an average F1 
      score of 94% for the six unitary behavior classes. The F1 score of movements is 
      78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework 
      offers a possibility to explore more detailed movements based on the unitary 
      behavior classification.
FAU - Li, Yongfeng
AU  - Li Y
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
AD  - AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio 
      Tech, University of Liege, 5030 Gembloux, Belgium.
FAU - Shu, Hang
AU  - Shu H
AUID- ORCID: 0000-0002-7124-3873
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
AD  - AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio 
      Tech, University of Liege, 5030 Gembloux, Belgium.
FAU - Bindelle, Jerome
AU  - Bindelle J
AUID- ORCID: 0000-0001-6974-4313
AD  - AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio 
      Tech, University of Liege, 5030 Gembloux, Belgium.
FAU - Xu, Beibei
AU  - Xu B
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
FAU - Zhang, Wenju
AU  - Zhang W
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
FAU - Jin, Zhongming
AU  - Jin Z
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
FAU - Guo, Leifeng
AU  - Guo L
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
FAU - Wang, Wensheng
AU  - Wang W
AD  - Agricultural Information Institute, Chinese Academy of Agriculture Sciences, 
      Beijing 100086, China.
LA  - eng
GR  - 2020ZD0004/Inner Mongolia Autonomous Region Science and Technology Major Project/
GR  - 202103250035/China Scholarships Council/
PT  - Journal Article
DEP - 20220420
PL  - Switzerland
TA  - Animals (Basel)
JT  - Animals : an open access journal from MDPI
JID - 101635614
PMC - PMC9104713
OTO - NOTNLM
OT  - behavior classification
OT  - inertial measurement units
OT  - machine learning
OT  - precision livestock farming
COIS- The authors declare no conflict of interest.
EDAT- 2022/05/15 06:00
MHDA- 2022/05/15 06:01
PMCR- 2022/04/20
CRDT- 2022/05/14 01:19
PHST- 2022/01/17 00:00 [received]
PHST- 2022/03/22 00:00 [revised]
PHST- 2022/04/15 00:00 [accepted]
PHST- 2022/05/14 01:19 [entrez]
PHST- 2022/05/15 06:00 [pubmed]
PHST- 2022/05/15 06:01 [medline]
PHST- 2022/04/20 00:00 [pmc-release]
AID - ani12091060 [pii]
AID - animals-12-01060 [pii]
AID - 10.3390/ani12091060 [doi]
PST - epublish
SO  - Animals (Basel). 2022 Apr 20;12(9):1060. doi: 10.3390/ani12091060.