Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

作     者：ZHANG Yu LI Xiangting SUN Yalin XUE Aidi ZHANG Yi JIANG Hailong SHEN Weizheng 张宇;李相廷;孙雅琳;薛爱迪;张翼;姜海龙;沈维政

作者机构：College of Electrical and InformationNortheast Agricultural UniversityHarbin 150030China Harbin Aerospace Stellar Data System Technology Co.LtdHarbin 150030China Harbin Electric Machinery Company Co.Ltd.Harbin 150030China 

基　　金：国家重点研发计划项目(2023YFD2000700) 财政部和农业农村部:国家现代农业产业技术体系资助(CARS36) 

出 版 物：《智慧农业（中英文）》 (Smart Agriculture)

年 卷 期：2024年第6卷第4期

页      码：29-41页

摘      要：[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow ***,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring ***‐ever,the application of edge device gives rise to the issue of inadequate real-time *** reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in *** on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant *** on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention ***,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation *** the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow rumin

主 题 词：cow rumination behavior real-time monitoring edge computing improved MobileNet v3 edge intelligence model Bi-LSTM 

学科分类：1305[艺术学-设计学类] 13[艺术学] 0905[农学-林学类] 081104[081104] 08[工学] 09[农学] 0804[工学-材料学] 081101[081101] 0811[工学-水利类] 

核心收录：

D　O　I：10.12133/j.smartag.SA202405023

馆 藏 号：203137292...