A Non-Invasive Alternative to RFID: Self-Sufficient 3D Identification of Group-Housed Livestock

TL;DR

This paper presents a non-invasive, vision-based 3D point cloud system for accurately identifying individual farm animals in group settings, offering a robust alternative to RFID tags.

Contribution

It introduces TARA, a semi-supervised, self-adaptive framework that maintains identity over time using 3D data and pseudo-labeling, improving livestock identification.

Findings

Achieved 100% visit-level identification accuracy on a sow dataset.

Demonstrated robustness of the vision-based system compared to RFID.

Enabled autonomous, non-intrusive animal monitoring.

Abstract

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.