IoT 23(2): e5


ResNet-based dairy daily behavior recognition

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  • @ARTICLE{10.4108/eetiot.v9i2.2901,
        author={Lijun Cheng and C. Jing and T. H. Duan and F. Z. Li},
        title={ResNet-based dairy daily behavior recognition},
        journal={EAI Endorsed Transactions on Internet of Things},
        keywords={Behavior recognition, ResNet, Cow Daily Behavior Dataset},
  • Lijun Cheng
    C. Jing
    T. H. Duan
    F. Z. Li
    Year: 2023
    ResNet-based dairy daily behavior recognition
    DOI: 10.4108/eetiot.v9i2.2901
Lijun Cheng1,*, C. Jing1, T. H. Duan1, F. Z. Li1
  • 1: Shanxi Agricultural University
*Contact email:


With the rapid development of China ’s dairy farming industry, it becomes difficult to breed and manage the increasing number of dairy cows. The smart agricultural enabled by edging techniques such as smart sensor, IoT, machine learning, etc. shows great potential to improve the scientific breeding and management of dairy cows. Using machine learning assisted computer vision to identify and classify the behavior of dairy cows can quickly determine the health status of dairy cows and improve management efficiency. However, there are still some challenges need to be addressed in the current behavior recognition of dairy cows. Due to the more complex background of dairy farms, the increase in the number of cows makes the mutual shading problem of dairy cows serious, which leads to the low efficiency of dairy cow behavior recognition. To address this challenge, this paper collected and labeled four types of 1,660 dairy daily behavior datasets and proposed a residual neural network (ResNet)-based dairy daily behavior recognition model. Experiments show that the proposed method is far superior to the baseline method in accuracy performance, and it provides inspiration for the behavior recognition of cows.