Resting Posture Recognition Method for Suckling Piglets Based on Piglet Posture Recognition (PPR)–You Only Look Once

Jinxin Chen, Luo Liu, Peng Li, Wen Yao, Mingxia Shen and Longshen Liu ()
Additional contact information
Jinxin Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Luo Liu: College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China
Peng Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Wen Yao: College of Animal Science & Technology, Nanjing Agricultural University, Nanjing 210095, 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 3, 1-24

Abstract: The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to enhance the detection accuracy and classification capability for different piglet resting postures. Firstly, to address the issue of numerous sows and piglets in the farrowing house that easily occlude each other, an image edge detection algorithm is employed to precisely locate the sow’s farrowing bed area. By cropping the images, irrelevant background interference is reduced, thereby enhancing the model’s recognition accuracy. Secondly, to overcome the limitations of the YOLOv11 model in fine feature extraction and small object detection, improvements are made, resulting in the proposed PPR-YOLO model. Specific enhancements include the introduction of a multi-branch Conv2 module to enrich feature extraction capabilities and the adoption of an inverted bottleneck IBCNeck module, which expands the number of channels and incorporates a channel attention mechanism. This strengthens the model’s ability to capture and differentiate subtle posture features. Additionally, in the post-processing stage, the relative positions between sows and piglets are utilized to filter out piglets located outside the sow region, eliminating interference from sow nursing behaviors in resting posture recognition, thereby ensuring the accuracy of posture classification. The experimental results show that the proposed method achieves accurate piglet posture recognition, outperforming mainstream object detection algorithms. Ablation experiments validate the effectiveness of image cropping and model enhancements in improving performance. This method provides effective technical support for the automated monitoring of piglet welfare in commercial farms and holds promising application prospects.

Keywords: PPR-YOLO; piglet posture recognition; deep learning; computer vision; animal welfare (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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/3/230/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/3/230/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:3:p:230-:d:1572773

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().