Short-Term Longitudinal Biometric Identification of Cattle Using Planum Nasolabiale Images and a Two-Stage Deep Learning Framework

Abstract

Simple Summary Reliable individual identification is fundamental to cattle traceability, herd management, disease control, and animal welfare. Conventional identifiers such as ear tags remain essential but may be lost, damaged, or difficult to read. The planum nasolabiale (PN), the hairless surface between the nostrils and upper lip, has an individually distinctive pattern that can be photographed without touching the animal. This study trained a two-stage computer-vision system using baseline PN images and tested whether the same 48 cattle could be re-identified in a fully separate imaging session 131 days later. The cohort included 32 adult cows and 16 calves, allowing short-term evaluation during a period in which growth-related change was expected to be more pronounced in the calves. The system correctly classified 185 of 192 follow-up images, corresponding to 96.35% overall longitudinal re-identification accuracy (95% CI: 92.67–98.22%). The system achieved 96.3% overall re-identification accuracy on the 192 follow-up images. These findings support PN imaging as a promising non-invasive complement to established cattle identification methods, while longer follow-up, multi-farm validation, age-stratified analysis, and open-set testing remain necessary before routine deployment.