Research of Estrus Detection Models in Dairy Cows by Activity

Daoerji Fan; Huijuan Wu

doi:doi:10.11648/j.sd.20180602.15

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Research of Estrus Detection Models in Dairy Cows by Activity

Daoerji Fan, Huijuan Wu

Published in Science Discovery (Volume 6, Issue 2)

Received: 20 June 2018 Published: 22 June 2018

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Abstract

The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.

Published in	Science Discovery (Volume 6, Issue 2)
DOI	10.11648/j.sd.20180602.15
Page(s)	102-109
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Cows Estrus Prediction, Activity, Logistic Regression, Multilayer Perceptions, Support Vector Machine

References

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[5]	Audrey C，Stéphanie C, Julie G et al. Comparison of three devices for the automated detection of estrus in dairy cows [J]. Theriogenology, 2014, 82(5): 734~741.
[6]	Reith S, Brandt H, Hoy S. Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period [J]. Livestock Science, 2014, 170(1): 219~227.
[7]	田富洋，王冉冉，刘莫尘，等.基于神经网络的奶牛发情行为辨识与预测研究[J].农业机械学报，2013，44(z1):277~281。
[8]	尹令，洪添胜，刘迎湖，等.基于无线传感器网络支持向量机奶牛行为特征识别[J].传感技术学报，2011，24(3):458~462。
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[12]	侯云涛,蔡晓华,吴泽全,等.奶牛行为特征识别方法的研究与实现—基于支持向量机[J].农机化研究, 2018, 40(8):36-41。
[13]	张爽.奶牛个体反刍行为监测技术研究[D].东北农业大学,2017。
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APA Style

Daoerji Fan, Huijuan Wu. (2018). Research of Estrus Detection Models in Dairy Cows by Activity. Science Discovery, 6(2), 102-109. https://doi.org/10.11648/j.sd.20180602.15

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ACS Style

Daoerji Fan; Huijuan Wu. Research of Estrus Detection Models in Dairy Cows by Activity. Sci. Discov. 2018, 6(2), 102-109. doi: 10.11648/j.sd.20180602.15

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AMA Style

Daoerji Fan, Huijuan Wu. Research of Estrus Detection Models in Dairy Cows by Activity. Sci Discov. 2018;6(2):102-109. doi: 10.11648/j.sd.20180602.15

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@article{10.11648/j.sd.20180602.15,
  author = {Daoerji Fan and Huijuan Wu},
  title = {Research of Estrus Detection Models in Dairy Cows by Activity},
  journal = {Science Discovery},
  volume = {6},
  number = {2},
  pages = {102-109},
  doi = {10.11648/j.sd.20180602.15},
  url = {https://doi.org/10.11648/j.sd.20180602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180602.15},
  abstract = {The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.},
 year = {2018}
}

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TY  - JOUR
T1  - Research of Estrus Detection Models in Dairy Cows by Activity
AU  - Daoerji Fan
AU  - Huijuan Wu
Y1  - 2018/06/22
PY  - 2018
N1  - https://doi.org/10.11648/j.sd.20180602.15
DO  - 10.11648/j.sd.20180602.15
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 102
EP  - 109
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20180602.15
AB  - The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.
VL  - 6
IS  - 2
ER  -

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Author Information

Daoerji Fan

College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China
Huijuan Wu

College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China

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APA Style

Daoerji Fan, Huijuan Wu. (2018). Research of Estrus Detection Models in Dairy Cows by Activity. Science Discovery, 6(2), 102-109. https://doi.org/10.11648/j.sd.20180602.15

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ACS Style

Daoerji Fan; Huijuan Wu. Research of Estrus Detection Models in Dairy Cows by Activity. Sci. Discov. 2018, 6(2), 102-109. doi: 10.11648/j.sd.20180602.15

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AMA Style

Daoerji Fan, Huijuan Wu. Research of Estrus Detection Models in Dairy Cows by Activity. Sci Discov. 2018;6(2):102-109. doi: 10.11648/j.sd.20180602.15

Copy | Download

@article{10.11648/j.sd.20180602.15,
  author = {Daoerji Fan and Huijuan Wu},
  title = {Research of Estrus Detection Models in Dairy Cows by Activity},
  journal = {Science Discovery},
  volume = {6},
  number = {2},
  pages = {102-109},
  doi = {10.11648/j.sd.20180602.15},
  url = {https://doi.org/10.11648/j.sd.20180602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180602.15},
  abstract = {The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Research of Estrus Detection Models in Dairy Cows by Activity
AU  - Daoerji Fan
AU  - Huijuan Wu
Y1  - 2018/06/22
PY  - 2018
N1  - https://doi.org/10.11648/j.sd.20180602.15
DO  - 10.11648/j.sd.20180602.15
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 102
EP  - 109
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20180602.15
AB  - The detection of estrus in large-scale ranch is an extremely labor-intensive task. Accurate and efficient detection can shorten the tires spacing of cow and increase economic income of ranch.The change in activity is one of the most important external features of dairy cows' estrus.In this paper, the activity of cows was collected remotely using activity collectors, wireless transmission networks and cloud servers, and the data was framed, labeled and preprocessed. The logistic regression, MLPs and SVMs model were trained by differential activity data of current and historical dates within the same hour.The experimental results show that the detection model by cow activity has a higher accuracy in predicting the estrus of cows. The SVMs in the three models have the best prediction effect. The recall and accuracy rate reach 90.12% and 93.74%, respectively.In the actual ranch test, the estrus detection system using the SVM model has more than double the exposure rate than the manual disclosure.
VL  - 6
IS  - 2
ER  -

Copy | Download