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S_T_Mamba: A Novel Jinnan Calf Diarrhea Behavior Recognition Model Based on Sequence Tree Mamba.

Wangli Hao1, Yakui Xue1, Hao Shu1

  • 1College of Software, Shanxi Agricultural University, Jinzhong 030801, China.

Animals : an Open Access Journal From MDPI
|September 27, 2025
PubMed
Summary

A new S_T_Mamba model accurately identifies Jinnan calf diarrhea by analyzing video sequences and pixel features. This advanced approach significantly improves early detection and animal health management.

Keywords:
behavior recognitionminimum spanning treesequence processing strategystate space model

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Area of Science:

  • Veterinary Medicine
  • Animal Behavior
  • Machine Learning

Background:

  • Accurate recognition of calf diarrhea is vital for livestock health.
  • Conventional methods struggle with subtle behavioral differences.

Purpose of the Study:

  • To introduce a novel model for recognizing diarrhea-related behaviors in Jinnan calves.
  • To enhance the precision of calf health monitoring through advanced AI.

Main Methods:

  • Developed the S_T_Mamba (Sequence Tree Mamba) model.
  • Utilized a sequence processing strategy for temporal dependencies.
  • Incorporated a tree state space module (TreeSSM) for long-range pixel feature aggregation.

Main Results:

  • S_T_Mamba achieved 99.78% accuracy in Jinnan calf diarrhea behavior recognition.
  • The model outperformed existing methods by 0.59% to 1.99%.
  • Demonstrated superior ability to distinguish between similar behavioral patterns.

Conclusions:

  • The S_T_Mamba model offers a significant advancement in calf diarrhea behavior recognition.
  • This technology can improve early detection and management of calf health issues.
  • The model's effectiveness highlights the potential of advanced AI in precision livestock farming.