Estrous detection by continuous measurements of vaginal temperature and conductivity with supervised machine learning in cattle.
Cattle
Estrous detection
Supervised machine learning
Vaginal conductivity
Vaginal temperature
Wearable sensor
Journal
Theriogenology
ISSN: 1879-3231
Titre abrégé: Theriogenology
Pays: United States
ID NLM: 0421510
Informations de publication
Date de publication:
01 Jan 2019
01 Jan 2019
Historique:
received:
11
05
2018
revised:
22
09
2018
accepted:
26
09
2018
pubmed:
8
10
2018
medline:
17
1
2019
entrez:
8
10
2018
Statut:
ppublish
Résumé
This study aimed to evaluate the effectiveness of estrous detection technique based on continuous measurements of vaginal temperature (VT) and conductivity (VC) with supervised machine learning in cattle. The VT and VC of 17 cows in tie-stalls were measured using our developed wearable vaginal sensor from Day 11 (Day 0 = ovulation day) to Day 11 of the subsequent estrous cycle at 15-min interval. After the maximum VT and VC were extracted hourly, their changes were expressed as residual VT (rVT = actual VT - mean VT for the same time on the previous 3 days) and as VC ratio (VCr = actual VC/mean VC for the same time on Day 11-13), respectively, and were used for analysis. Trans-rectal ultrasonography was performed to monitor ovarian structure changes. The plasma concentrations of reproductive hormones (progesterone: P
Identifiants
pubmed: 30292860
pii: S0093-691X(18)30876-8
doi: 10.1016/j.theriogenology.2018.09.038
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
90-99Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.