Conference Paper

Status Discrimination of Dairy Cows using Activity Meter and Machine Learning

2018 International Conference on Computers and Their Applications
Hayato Ohwada, Hiroyuki Nishiyama, Yusuke Ono


 It is important to detect the estrus of a cow in order

to maintain lactation and increase farmer productivity.

In the existing method, the observation interval is long,

and it is possible to overlook the estrus. In this study,

we propose a method of discriminating the condition

of dairy cow using acceleration data. It is intended to

detect changes of behaviors in dairy cows at shorter

intervals and to enable precise estrus detection. We

attached a small acceleration sensor to a cow and

collected data. We extracted features from the collected

data and applied machine learning to predict the cows

condition. In this study, we attached sensors to dairy

cows at Arimura Farm and the Konsen Agricultural

Experiment Station, and collected data. One dairy cow

at each farm had a sensor attached. We extracted the

feature quantity and discriminated the state and the

10- fold cross validation. It was possible to judge the

state with an accuracy of 90% or more in collected data

on both farms. In addition, when the collected data

on both farms were combined and learned, it became

possible to judge the state with an accuracy of 94.8%.

CATA 2018

Las Vegas, NV, USA
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