农业机械学报2026，Vol.57Issue(15)：56-63,85,9.DOI:10.6041/j.issn.1000-1298.2026.15.005
基于SPR-Net的哺乳母猪分娩前行为识别方法
Recognition of Pre-farrowing Behavior in Sows Based on SPR-Net
摘要
Abstract
Accurate recognition of prepartum behaviors in sows is of great significance for farrowing prediction.An SPR-Net network was proposed for sow posture recognition.The network employed HRNet as the backbone and integrated a feature pyramid network with a token-based two-stage cross-attention module in the neck.Meanwhile,a keypoint head loss was introduced into the loss function as an inductive bias,and asymmetric weighting was applied to constrain the classification head.On the test set,SPR-Net achieved a Top-1 accuracy of 95.6%for the classification of four sow postures.After performance validation,the network was further used to investigate prepartum behavioral patterns of sows with different parities.The results showed that the posture transition frequency of both low-parity and high-parity sows exhibited a staged increasing trend before farrowing.Moreover,the posture transition frequency of low-parity sows was significantly higher than that of high-parity sows during the early and late prepartum stages,whereas no significant difference was observed between the two groups during the middle stage.In addition,low-parity and high-parity sows showed different ranges of posture frequency variation before farrowing.These behavioral characteristics can serve as indicators of impending parturition,helping farms take timely management measures.Overall,SPR-Net demonstrated excellent performance in prepartum behavior recognition of sows,and video-based analysis revealed parity-related prepartum behavioral patterns.The research result can provide strong technical support and theoretical evidence for achieving accurate farrowing prediction in lactating sows.关键词
母猪分娩/姿态识别/交叉注意力/损失加权/HRNetKey words
sow farrowing/posture recognition/cross-attention/loss weighting/HRNet分类
农业科技引用本文复制引用
汪博,陈德恩,许俊华,洪林君,张素敏,尹令..基于SPR-Net的哺乳母猪分娩前行为识别方法[J].农业机械学报,2026,57(15):56-63,85,9.基金项目
农业科技重大项目(NK20221101) （NK20221101）
