This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
S_T_Mamba: A Novel Jinnan Calf Diarrhea Behavior Recognition Model Based on Sequence Tree Mamba
by
, , ,
Wangli Hao
1,*
,
Yakui Xue
1,
Hao Shu
1,
Bingxue Lv
2,
Hanwei Li
2,
Meng Han
3,
Yanhong Liu
2,* and
Fuzhong Li
1 1
College of Software, Shanxi Agricultural University, Jinzhong 030801, China
2
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
3
Hangzhou Economic Development Zone, Hangzhou Dianzi University, Hangzhou 310018, China
*
Authors to whom correspondence should be addressed.
Animals 2025, 15(18), 2646; https://doi.org/10.3390/ani15182646
Submission received: 10 August 2025
/
Revised: 4 September 2025
/
Accepted: 8 September 2025
/
Published: 9 September 2025
(This article belongs to the Section Animal System and Management)
Simple Summary
Calf diarrhea behavior is a reliable indicator of health status, and accurate behavior recognition is vital for effective health surveillance and management. However, current behavior recognition techniques often struggle to distinguish between similar behaviors, leading to a decrease in performance. This research proposes a new model, named S_T_Mamba, which leverages a sequence processing strategy and a tree state space module to capture temporal dependencies and long-range pixel associations in video frames. The model can effectively recognize subtle differences in calf diarrhea behavior. Consequently, the performance of calf diarrhea behavior recognition is enhanced by the S_T_Mamba model.
Abstract
The efficient and precise recognition of diarrhea-related behaviors in Jinnan calves is crucial for ensuring their healthy development. Nevertheless, conventional behavior recognition techniques are often limited by a notable decline in performance when distinguishing between similar behavioral patterns. This paper proposes a novel behavior recognition model for Jinnan calf diarrhea, named S_T_Mamba (Sequence Tree Mamba). Specifically, S_T_Mamba incorporates a sequence processing strategy and a tree state space module (TreeSSM). The sequence processing strategy utilizes sequence as inputs to capture the temporal dependencies underlying the video. Additionally, the tree state space module is designed to extract and aggregate long-range pixel association features from video frames, enabling the effective recognition of subtle distinctions between similar behaviors. Therefore, the proposed model significantly enhances the performance of calf diarrhea behavior recognition. Experimental results indicate that the S_T_Mamba model achieves state-of-the-art performance in Jinnan calf diarrhea behavior recognition. Specifically, S_T_Mamba achieves 99.78% accuracy, outperforming existing popular models by 0.59% to 1.99%.
