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Smart Agriculture ›› 2024, Vol. 6 ›› Issue (4): 29-41.doi: 10.12133/j.smartag.SA202405023

• Topic--Technological Innovation and Sustainable Development of Smart Animal Husbandry • Previous Articles     Next Articles

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

ZHANG Yu1, LI Xiangting1, SUN Yalin2, XUE Aidi1,3, ZHANG Yi1, JIANG Hailong1, SHEN Weizheng1()   

  1. 1. College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
    2. Harbin Aerospace Stellar Data System Technology Co. , Ltd, Harbin 150030, China
    3. Harbin Electric Machinery Company Co. , Ltd. , Harbin 150030, China
  • Received:2024-05-30 Online:2024-07-30
  • Foundation items:
    The National Key Research and Development Program of China(2023YFD2000700); Supported by The Earmarked Fund for CARS36(CARS36)
  • About author:
    ZHANG Yu, research interests are agricultural AI and AIoT. E-mail:
  • corresponding author:
    SHEN Weizheng, PhD, Professor, research interest is smart livestock. E-mail:

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. However, 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] Autonomously 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 investigated 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 utilizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm. In the study on split edge intelligence, 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 comparative experiments with MobileNet v3 and MobileNet-LSTM, the federated edge intelligence model based on CA-MobileNet 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 applied in practical settings.

Key words: cow rumination behavior, real-time monitoring, edge computing, improved MobileNet v3, edge intelligence model, Bi-LSTM