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Automatic recognition method of cow ruminating behaviour based on edge computing.

Authors :
Shen, Weizheng
Sun, Yalin
Zhang, Yu
Fu, Xiao
Hou, Handan
Kou, Shengli
Zhang, Yonggen
Source :
Computers & Electronics in Agriculture. Dec2021, Vol. 191, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• The edge device monitors the ruminating behaviour of cows in real-time. • The adaptive threshold makes ruminating monitoring for individual cows. • Edge computing reduces the amount of data transmission and cloud computing. • The ruminating information is uploaded to the cloud for further aggregation. Timely monitoring of the ruminating behaviour of dairy cows is beneficial for obtaining relevant information on dairy cow health to predict dairy cow diseases for the first time. To date, various strategies for monitoring ruminating behaviour have been proposed, but the real-time monitoring of these strategies has not been fully realized. Based on edge computing, we proposed a real-time method to monitor the ruminating behaviour of dairy cows. In this work, a self-designed edge device was used to collect and process the three-axis acceleration signals of dairy cows in real-time, and then a rumination recognition algorithm was used to calculate the overall sliding geometric mean of the Euclidean distance between the feature sets in real-time, determine the adaptive threshold, and verify the ruminating behaviour by the sliding window. Finally, real-time recognition of the ruminating behaviour of dairy cows was completed on the edge device side, without requiring substantial calculation time and resources. The edge device uploaded cow ruminating information to the cloud in real-time every two hours, and the cloud further aggregated the ruminating information. Compared with the traditional method of uploading three-axis acceleration data, this cloud-end integrated system based on edge computing reduced the amount of uploaded data bytes by 99.9%. Our ruminating recognition has achieved the following performance values: precision (93.7%), recall (92.8%), F1-score (93.3%), specificity (97.4%) and accuracy (96.1%), indicating a good classification effect. This research provides a real-time and effective method for monitoring of cow ruminating behaviour, and the proposed system can be used in practical applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01681699
Volume :
191
Database :
Academic Search Index
Journal :
Computers & Electronics in Agriculture
Publication Type :
Academic Journal
Accession number :
153852270
Full Text :
https://doi.org/10.1016/j.compag.2021.106495