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Improved YOLO-Goose-Based Method for Individual Identification of Lion-Head Geese and Egg Matching: Methods and Experimental Study

文献类型： 外文期刊

作者： Zhang, Hengyuan 1 ; Wu, Zhenlong 1 ; Zhang, Tiemin 1 ; Lu, Canhuan 1 ; Zhang, Zhaohui 1 ; Ye, Jianzhou 1 ; Yang, Jikang 1 ; Yang, Degui 5 ; Fang, Cheng 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangdong Lab Lingnan Modern Agr, Guangzhou 510642, Peoples R China

2.State Key Lab Livestock & Poultry Breeding, Guangzhou 510642, Peoples R China

3.Katholieke Univ Leuven KU LEUVEN, Fac Biosci Engn, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium

4.Natl Engn Res Ctr Breeding Swine Ind, Guangzhou 510642, Peoples R China

5.South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China

关键词： Lion-Head Geese; object detection; computer vision; precision livestock farming; goose egg identification and assignment

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 13 期

页码：

收录情况： SCI

摘要： As a crucial characteristic waterfowl breed, the egg-laying performance of Lion-Headed Geese serves as a core indicator for precision breeding. Under large-scale flat rearing and selection practices, high phenotypic similarity among individuals within the same pedigree coupled with traditional manual observation and existing automation systems relying on fixed nesting boxes or RFID tags has posed challenges in achieving accurate goose-egg matching in dynamic environments, leading to inefficient individual selection. To address this, this study proposes YOLO-Goose, an improved YOLOv8s-based method, which designs five high-contrast neck rings (DoubleBar, Circle, Dot, Fence, Cylindrical) as individual identifiers. The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. Experimental results show that the model achieves an F1 score of 93.8% and mAP50 of 96.4% on the self-built dataset, representing increases of 10.1% and 5% compared to the original YOLOv8s, with a 27.1% reduction in computational load. The dynamic matching algorithm, incorporating spatiotemporal trajectories and egg positional data, achieves a 95% matching rate, a 94.7% matching accuracy, and a 5.3% mismatching rate. Through lightweight deployment using TensorRT, the inference speed is enhanced by 1.4 times compared to PyTorch-1.12.1, with detection results uploaded to a cloud database in real time. This solution overcomes the technical bottleneck of individual selection in flat rearing environments, providing an innovative computer-vision-based approach for precision breeding of pedigree Lion-Headed Geese and offering significant engineering value for advancing intelligent waterfowl breeding.

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