Related Experiment Video
Updated: Jan 15, 2026
08:00
Decoding Natural Behavior from Neuroethological Embedding
Published on: October 3, 2025
587
EnhancedMulti-Scenario Pig Behavior Recognition Based on YOLOv8n
Panqi Pu1, Junge Wang1, Geqi Yan1
1Key Laboratory of Efficient Utilization of Non-Grain Feed Resources (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shandong Provincial Key Laboratory of Animal Nutrition and Efficient Feeding, Department of Animal Science, Shandong Agricultural University, Tai'an 271017, China.
Animals : an Open Access Journal From MDPI
|October 16, 2025
Summary
This study introduces an improved YOLOv8n model for efficient pig behavior monitoring in smart animal husbandry. The enhanced model achieves high accuracy in recognizing key pig behaviors, supporting non-invasive health anomaly detection.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Animal Science
Background:
- Smart animal husbandry requires efficient pig behavior monitoring.
- Traditional methods are operationally inefficient and cause animal stress.
Purpose of the Study:
- To develop a lightweight, high-precision model for real-time pig behavior recognition.
- To improve monitoring efficiency and reduce animal stress in commercial piggeries.
Main Methods:
- Utilized a lightweight YOLOv8n architecture.
- Incorporated SPD-Conv for feature preservation and LSKBlock attention for feature fusion.
- Developed a dedicated small-target detection head for enhanced accuracy.
Main Results:
- Achieved 92.4% mean average precision (mAP@0.5) and 87.4% recall.
- Outperformed baseline YOLOv8n by 3.7% in AP with minimal parameter increase (3.34M).
- Demonstrated enhanced robustness under varying illumination conditions.
Conclusions:
- The optimized model enables real-time, non-invasive recognition of standing, lying, and feeding behaviors.
- Supports early health anomaly detection in commercial piggeries.
- Offers a significant advancement in smart animal husbandry monitoring systems.
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