A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses
Ventilation quality in summer layer houses is critical for heat stress prevention, production performance, and poultry welfare. Addressing the issue of ''qualified environmental parameters but chicken discomfort'' caused by traditional methods overlooking spatial heterogeneity an...
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Elsevier
2025-08-01
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| Schriftenreihe: | Poultry Science |
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| author | Zixuan Zhou Lihua Li Hao Xue Yuchen Jia Yao Yu Zongkui Xie Yuhan Gu |
| author_facet | Zixuan Zhou Lihua Li Hao Xue Yuchen Jia Yao Yu Zongkui Xie Yuhan Gu |
| author_sort | Zixuan Zhou |
| collection | DOAJ |
| description | Ventilation quality in summer layer houses is critical for heat stress prevention, production performance, and poultry welfare. Addressing the issue of ''qualified environmental parameters but chicken discomfort'' caused by traditional methods overlooking spatial heterogeneity and individual differences, a dynamic ventilation quality assessment method based on panting behavior detection in laying hens was proposed. The YOLOv10-BCE panting behavior detection model was developed by embedding the BiFormer module into the backbone network to enhance multi-dimensional feature extraction, compressing neck structure parameters using the C3Ghost module, and integrating Efficient Intersection over Union (EIOU) loss to improve detection accuracy and convergence speed. K-means clustering and linear regression algorithms were employed to establish a quantitative correlation curve between ventilation quality and panting behavior, forming a Normal-Alert-Danger ventilation quality (VQ) classification standard. Experimental results demonstrated that the YOLOv10-BCE model achieved a mean average precision (mAP) of 95.8 % and a detection speed of 0.2 ms, significantly outperforming comparative models such as Faster R-CNN, SSD, and YOLOv9. The ventilation quality correlation model showed high fitting accuracy with an R² value of 0.974. Significant physiological differences (p < 0.05) in chickens across VQ grades validated the model's discriminative ability. The method accurately identified latent ventilation anomalies and spatial dead zones in large-scale layer houses. After ventilation strategy optimization, panting prevalence decreased by 65 %, establishing a closed-loop ''monitoring-assessment-regulation'' dynamic feedback mechanism. This study provides a behavioral-quantitative assessment solution for summer layer house ventilation quality. |
| format | Article |
| id | doaj-art-af5667b8a01f4e9b895f9bcdfd9f7e7e |
| institution | Matheson Library |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-af5667b8a01f4e9b895f9bcdfd9f7e7e2025-07-23T05:23:34ZengElsevierPoultry Science0032-57912025-08-011048105371A panting behavior-driven assessment framework for summer ventilation quality optimization in layer housesZixuan Zhou0Lihua Li1Hao Xue2Yuchen Jia3Yao Yu4Zongkui Xie5Yuhan Gu6College 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, ChinaCollege 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; Corresponding author at: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China.College 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, ChinaCollege 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, ChinaCollege 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, ChinaCollege 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, ChinaCollege 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, ChinaVentilation quality in summer layer houses is critical for heat stress prevention, production performance, and poultry welfare. Addressing the issue of ''qualified environmental parameters but chicken discomfort'' caused by traditional methods overlooking spatial heterogeneity and individual differences, a dynamic ventilation quality assessment method based on panting behavior detection in laying hens was proposed. The YOLOv10-BCE panting behavior detection model was developed by embedding the BiFormer module into the backbone network to enhance multi-dimensional feature extraction, compressing neck structure parameters using the C3Ghost module, and integrating Efficient Intersection over Union (EIOU) loss to improve detection accuracy and convergence speed. K-means clustering and linear regression algorithms were employed to establish a quantitative correlation curve between ventilation quality and panting behavior, forming a Normal-Alert-Danger ventilation quality (VQ) classification standard. Experimental results demonstrated that the YOLOv10-BCE model achieved a mean average precision (mAP) of 95.8 % and a detection speed of 0.2 ms, significantly outperforming comparative models such as Faster R-CNN, SSD, and YOLOv9. The ventilation quality correlation model showed high fitting accuracy with an R² value of 0.974. Significant physiological differences (p < 0.05) in chickens across VQ grades validated the model's discriminative ability. The method accurately identified latent ventilation anomalies and spatial dead zones in large-scale layer houses. After ventilation strategy optimization, panting prevalence decreased by 65 %, establishing a closed-loop ''monitoring-assessment-regulation'' dynamic feedback mechanism. This study provides a behavioral-quantitative assessment solution for summer layer house ventilation quality.http://www.sciencedirect.com/science/article/pii/S0032579125006145Laying hensMultimodalMachine learningBehavior detectionVentilation quality assessment |
| spellingShingle | Zixuan Zhou Lihua Li Hao Xue Yuchen Jia Yao Yu Zongkui Xie Yuhan Gu A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses Poultry Science Laying hens Multimodal Machine learning Behavior detection Ventilation quality assessment |
| title | A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses |
| title_full | A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses |
| title_fullStr | A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses |
| title_full_unstemmed | A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses |
| title_short | A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses |
| title_sort | panting behavior driven assessment framework for summer ventilation quality optimization in layer houses |
| topic | Laying hens Multimodal Machine learning Behavior detection Ventilation quality assessment |
| url | http://www.sciencedirect.com/science/article/pii/S0032579125006145 |
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