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A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses.

Zixuan Zhou1, Lihua Li1, Hao Xue1

  • 1College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China.

Poultry Science
|June 6, 2025
PubMed
Summary

A new method uses panting behavior detection in laying hens to assess ventilation quality in summer layer houses, improving poultry welfare and preventing heat stress. This dynamic feedback system optimizes ventilation strategies for better chicken comfort and performance.

Keywords:
Behavior detectionLaying hensMachine learningMultimodalVentilation quality assessment

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Area of Science:

  • Agricultural Engineering
  • Animal Science
  • Environmental Science

Background:

  • Traditional ventilation assessment in layer houses often fails to account for spatial variations and individual chicken needs, leading to heat stress despite seemingly adequate environmental parameters.
  • Ensuring optimal ventilation quality is crucial for preventing heat stress, maintaining production efficiency, and safeguarding poultry welfare during summer months.
  • Existing methods overlook the direct physiological responses of chickens, necessitating a more dynamic and behavior-based assessment approach.

Purpose of the Study:

  • To develop a dynamic ventilation quality assessment method for summer layer houses by detecting panting behavior in laying hens.
  • To create a robust panting behavior detection model (YOLOv10-BCE) capable of accurate and rapid identification.
  • To establish a quantitative correlation between detected panting behavior and ventilation quality, leading to a practical classification standard.

Main Methods:

  • Developed the YOLOv10-BCE model incorporating BiFormer and C3Ghost modules for enhanced feature extraction and parameter compression, utilizing Efficient Intersection over Union (EIOU) loss.
  • Employed K-means clustering and linear regression to correlate panting behavior frequency with ventilation quality, establishing a Normal-Alert-Danger (VQ) classification.
  • Validated the model's performance against established object detection models (Faster R-CNN, SSD, YOLOv9) and assessed physiological differences in chickens across VQ grades.

Main Results:

  • The YOLOv10-BCE model achieved a mean average precision (mAP) of 95.8% and a detection speed of 0.2 ms, surpassing other models.
  • The ventilation quality correlation model demonstrated high accuracy with an R² value of 0.974.
  • Optimized ventilation strategies based on the new assessment method reduced panting prevalence by 65%, confirming the model's effectiveness in identifying and rectifying ventilation issues.

Conclusions:

  • The proposed behavioral-quantitative method offers a novel and effective solution for dynamic ventilation quality assessment in large-scale layer houses.
  • The YOLOv10-BCE model provides a highly accurate and efficient tool for detecting panting behavior, a key indicator of heat stress.
  • Implementation of this method establishes a closed-loop system for monitoring, assessment, and regulation, significantly improving poultry welfare and farm management during hot weather.