An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time
| Autor: | Ikuo Kobayashi, Pyke Tin, Thi Thi Zin, Yoichiro Horii, Swe Zar Maw |
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| Rok vydání: | 2021 |
| Předmět: |
Transient state
Computer science prediction of calving time Ice calving TP1-1185 Prediction system Markov model Biochemistry Article Analytical Chemistry Machine Learning Absorbing Markov chain Pregnancy absorbing Markov chain Statistics Animals Transient (computer programming) Electrical and Electronic Engineering Instrumentation Monitoring Physiologic Event (probability theory) Behavior Animal Markov chain Chemical technology Parturition cow behavior analysis Markov Chains Atomic and Molecular Physics and Optics Cattle Female |
| Zdroj: | Sensors Volume 21 Issue 19 Sensors, Vol 21, Iss 6490, p 6490 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21196490 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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