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An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time
Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512676/ https://www.ncbi.nlm.nih.gov/pubmed/34640810 http://dx.doi.org/10.3390/s21196490 |
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author | Maw, Swe Zar Zin, Thi Thi Tin, Pyke Kobayashi, Ikuo Horii, Yoichiro |
author_facet | Maw, Swe Zar Zin, Thi Thi Tin, Pyke Kobayashi, Ikuo Horii, Yoichiro |
author_sort | Maw, Swe Zar |
collection | PubMed |
description | Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising. |
format | Online Article Text |
id | pubmed-8512676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85126762021-10-14 An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time Maw, Swe Zar Zin, Thi Thi Tin, Pyke Kobayashi, Ikuo Horii, Yoichiro Sensors (Basel) Article Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising. MDPI 2021-09-28 /pmc/articles/PMC8512676/ /pubmed/34640810 http://dx.doi.org/10.3390/s21196490 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Maw, Swe Zar Zin, Thi Thi Tin, Pyke Kobayashi, Ikuo Horii, Yoichiro An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title_full | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title_fullStr | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title_full_unstemmed | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title_short | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
title_sort | absorbing markov chain model to predict dairy cow calving time |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512676/ https://www.ncbi.nlm.nih.gov/pubmed/34640810 http://dx.doi.org/10.3390/s21196490 |
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