SPMF-YOLO-Tracker: A Method for Quantifying Individual Activity Levels and Assessing Health in Newborn Piglets

Jingge Wei, Yurong Tang, Jinxin Chen, Kelin Wang, Peng Li, Mingxia Shen and Longshen Liu ()
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Jingge Wei: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yurong Tang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jinxin Chen: College of Information Engineering, Suqian University, Suqian 223800, China
Kelin Wang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Peng Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Mingxia Shen: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Longshen Liu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 19, 1-21

Abstract: This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the MFM module, and the NWD loss function into YOLOv11. When combined with the ByteTrack algorithm, it achieves stable tracking and maintains trajectory continuity for multiple targets. An annotated dataset containing both detection and tracking labels was constructed using video data from 10 piglet pens for evaluation. Experimental results indicate that SPMF-YOLO achieved a recognition accuracy rate of 95.3% for newborn piglets. When integrated with ByteTrack, it achieves 79.1% HOTA, 92.2% MOTA, and 84.7% IDF1 in multi-object tracking tasks, thereby outperforming existing methods. Building upon this foundation, this study further quantified the cumulative movement distance of each newborn piglet within 30 min after birth and proposed a health-assessment method based on statistical thresholds. The results demonstrated an overall consistency rate of 98.2% across pens and an accuracy rate of 92.9% for identifying abnormal individuals. The results validated the effectiveness of this method for quantifying individual behavior and assessing health status in newborn piglets within complex farming environments, providing a feasible technical pathway and scientific basis for health management and early intervention in precision animal husbandry.

Keywords: SPMF-YOLO; ByteTrack; precision livestock farming; deep learning; computer vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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