Research on Broiler Mortality Identification Methods Based on Video and Broiler Historical Movement

Hongyun Hao, Fanglei Zou, Enze Duan, Xijie Lei, Liangju Wang and Hongying Wang ()
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Hongyun Hao: Department of Engineering, China Agricultural University, Beijing 100083, China
Fanglei Zou: Department of Engineering, China Agricultural University, Beijing 100083, China
Enze Duan: Agricultural Facilities and Equipment study Institute, Jiangsu Academy of Agriculture Science, Nanjing 210014, China
Xijie Lei: Department of Engineering, China Agricultural University, Beijing 100083, China
Liangju Wang: Department of Engineering, China Agricultural University, Beijing 100083, China
Hongying Wang: Department of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 3, 1-21

Abstract: The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within the flock in videos. Dead broilers were identified through the analysis of the historical movement information of each broiler in the video. Firstly, the frame difference method was utilized to capture key frames in the video. An enhanced segmentation network, YOLOv8-SP, was then developed to obtain the mask coordinates of each broiler, and an optical flow estimation method was employed to generate optical flow maps and evaluate their movement. An average optical flow intensity (AOFI) index of broilers was defined and calculated to evaluate the motion level of each broiler in each key frame. With the AOFI threshold, broilers in the key frames were classified into candidate dead broilers and active live broilers. Ultimately, the identification of dead broilers was achieved by analyzing the frequency of each broiler being judged as a candidate death in all key frames within the video. We incorporated the parallelized patch-aware attention (PPA) module into the backbone network and improved the overlaps function with the custom power transform (PT) function. The box and mask segmentation mAP of the YOLOv8-SP model increased by 1.9% and 1.8%, respectively. The model’s target recognition performance for small targets and partially occluded targets was effectively improved. False and missed detections of dead broilers occurred in 4 of the 30 broiler testing videos, and the accuracy of the dead broiler identification algorithm proposed in this study was 86.7%.

Keywords: segmentation; optical flow; activity; video; YOLOv8; poultry (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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