Accurate identification of individual farm animals in group-housed environments is a cornerstone of precision livestock management. However, current industry standards rely heavily on Radio Frequency Identification (RFID) ear tags, which are invasive, prone to loss, and restricted by the spatial limitations of antenna fields. In this paper, we propose a non-intrusive, vision-based identification system leveraging 3D point cloud data captured within a commercial electronic feeding station (EFS). Departing from traditional supervised frame-level inference, we introduce the Temporal Adaptive Recognition Architecture (TARA), a self-sufficient, semi-supervised framework designed to maintain identity consistency over time. TARA employs a dynamic recalibration mechanism that updates individual identity profiles to account for morphological changes in the livestock. To facilitate training in label-scarce environments, we utilize a visit-level majority voting strategy to generate high-fidelity pseudo-labels from raw temporal sequences. Experimental results on a group housed sow dataset collected from an operational commercial barn demonstrate that our approach achieves 100% identification accuracy at the visit level. These results suggest that vision-based 3D point cloud analysis offers a robust, superior alternative to RFID-based systems, paving the way for fully autonomous individual animal monitoring.

翻译：准确识别群体饲养环境中的个体农场动物是精准畜牧业管理的基石。然而，当前行业标准高度依赖射频识别（RFID）耳标，这种方法具有侵入性、易丢失，且受限于天线场的空间范围。本文提出了一种基于视觉的非侵入式识别系统，利用在商业电子饲喂站（EFS）采集的三维点云数据。区别于传统的监督式帧级推理，我们引入了时间自适应识别架构（TARA），这是一种自给自足、半监督的框架，旨在维持长时间的身份一致性。TARA采用动态校准机制，通过更新个体身份配置文件来适应家畜的形态变化。为解决标签稀缺环境下的训练问题，我们利用访问级多数投票策略，从原始时间序列中生成高保真伪标签。在商业猪舍采集的群体饲养母猪数据集上的实验结果表明，我们的方法在访问级实现了100%的识别准确率。这些结果表明，基于视觉的三维点云分析能够提供比RFID系统更稳健、更优越的替代方案，为完全自主的个体动物监测铺平道路。