智慧农业（中英文）2024，Vol.6Issue(4)：29-41,13.DOI:10.12133/j.smartag.SA202405023
基于边缘计算和改进MobileNet v3的奶牛反刍行为实时监测方法
Real-Time Monitoring Method for Cow Rumination Behav-ior Based on Edge Computing and Improved MobileNet v3
摘要
Abstract
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How-ever,the application of edge device gives rise to the issue of inadequate real-time performance.To 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 real-time.Based 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 behavior.Focused 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 mechanism.Additionally,a federated edge intelligence model was designed uti-lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In 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 ruminant behavior,and the proposed federated edge intelligence model can be ap-plied in practical settings.关键词
奶牛反刍行为/实时监测/边缘计算/改进MobileNet v3/边缘智能模型/Bi-LSTMKey words
cow rumination behavior/real-time monitoring/edge computing/improved MobileNet v3/edge intelligence model/Bi-LSTM分类
农业科技引用本文复制引用
张宇,李相廷,孙雅琳,薛爱迪,张翼,姜海龙,沈维政..基于边缘计算和改进MobileNet v3的奶牛反刍行为实时监测方法[J].智慧农业（中英文）,2024,6(4):29-41,13.基金项目
The National Key Research and Development Program of China(2023YFD2000700) （2023YFD2000700）
Supported by The Earmarked Fund for CARS36(CARS36) 基金资助:国家重点研发计划项目(2023YFD2000700) （CARS36）
财政部和农业农村部:国家现代农业产业技术体系资助(CARS36) （CARS36）
