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Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers
Author(s) -
Tamura Tomoya,
Okubo Yuki,
Deguchi Yoshitaka,
Koshikawa Shizu,
Takahashi Masahiro,
Chida Yasushi,
Okada Keiji
Publication year - 2019
Publication title -
animal science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 38
eISSN - 1740-0929
pISSN - 1344-3941
DOI - 10.1111/asj.13184
Subject(s) - accelerometer , decision tree , rumination , collar , acceleration , tree (set theory) , dairy cattle , decision tree learning , computer science , mathematics , simulation , statistics , artificial intelligence , machine learning , zoology , psychology , engineering , biology , physics , structural engineering , mathematical analysis , cognition , classical mechanics , neuroscience , operating system
Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors ( p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.