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...
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Published in | Sensors (Basel, Switzerland) Vol. 21; no. 19; p. 6490 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Abstract | 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. |
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AbstractList | 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. |
Author | Horii, Yoichiro Kobayashi, Ikuo Maw, Swe Zar Tin, Pyke Zin, Thi Thi |
AuthorAffiliation | 4 Center of Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; horii@cc.miyazaki-u.ac.jp 1 Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan; z3t1802@student.miyazaki-u.ac.jp 2 Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan; pyketin11@gmail.com 3 Field Science Center, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan; ikuokob@cc.miyazaki-u.ac.jp |
AuthorAffiliation_xml | – name: 3 Field Science Center, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan; ikuokob@cc.miyazaki-u.ac.jp – name: 1 Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan; z3t1802@student.miyazaki-u.ac.jp – name: 2 Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan; pyketin11@gmail.com – name: 4 Center of Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; horii@cc.miyazaki-u.ac.jp |
Author_xml | – sequence: 1 givenname: Swe Zar orcidid: 0000-0003-1788-6314 surname: Maw fullname: Maw, Swe Zar – sequence: 2 givenname: Thi Thi orcidid: 0000-0003-3435-2197 surname: Zin fullname: Zin, Thi Thi – sequence: 3 givenname: Pyke orcidid: 0000-0002-3623-2984 surname: Tin fullname: Tin, Pyke – sequence: 4 givenname: Ikuo surname: Kobayashi fullname: Kobayashi, Ikuo – sequence: 5 givenname: Yoichiro surname: Horii fullname: Horii, Yoichiro |
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SubjectTerms | absorbing Markov chain Animals Artificial intelligence Behavior Cameras Change detection cow behavior analysis Dairy cattle Dairy farming Dairy farms Dairy industry Image processing Internet of Things Machine learning Markov analysis Markov chains prediction of calving time Sensors Statistical analysis |
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Title | An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time |
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