Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications

TL;DR

This work tackles the problem of reliable, cross-breed pig identification suitable for resource-constrained, small-scale farms. It introduces a multistage computer vision pipeline that extracts a 68-dimensional vein-feature vector from auricular vein patterns and classifies identity with four ML models, with SVM achieving 98.12% precision on mixed-breed pigs. The approach emphasizes noninvasiveness and smartphone deployability, delivering an end-to-end processing time of about 8.3 seconds. The results demonstrate practical feasibility for precision livestock management and lay groundwork for broader adoption in diverse farm environments, while highlighting opportunities for temporal analysis and multimodal fusion to further enhance robustness.

Abstract

Accurate livestock identification is a cornerstone of modern farming: it supports health monitoring, breeding programs, and productivity tracking. However, common pig identification methods, such as ear tags and microchips, are often unreliable, costly, target pure breeds, and thus impractical for small-scale farmers. To address this gap, we propose a noninvasive biometric identification approach that leverages uniqueness of the auricular vein patterns. To this end, we have collected 800 ear images from 20 mixed-breed pigs (Landrace cross Pietrain and Duroc cross Pietrain), captured using a standard smartphone and simple back lighting. A multistage computer vision pipeline was developed to enhance vein visibility, extract structural and spatial features, and generate biometric signatures. These features were then classified using machine learning models. Support Vector Machines (SVM) achieved the highest accuracy: correctly identifying pigs with 98.12% precision across mixed-breed populations. The entire process from image processing to classification was completed in an average of 8.3 seconds, demonstrating feasibility for real-time farm deployment. We believe that by replacing fragile physical identifiers with permanent biological markers, this system provides farmers with a cost-effective and stress-free method of animal identification. More broadly, the findings confirm the practicality of auricular vein biometrics for digitizing livestock management, reinforcing its potential to extend the benefits of precision farming to resource-constrained agricultural communities.

Figures (4)

• Figure 1: Complete pipeline architecture for pig identification using auricular vein patterns, illustrating the sequential processing stages from raw ear image acquisition through ROI extraction, vein feature extraction, feature vector generation, to final machine learning classification for individual pig identification.
• Figure 2: ROI extraction process showing: (a) original ear image, (b) refined mask after morphological operations, and (c) final masked inner ear region for vein analysis.
• Figure 3: Vein extraction pipeline showing: (left) original ear image, (center) masked inner ear region, and (right) extracted vein patterns after processing.
• Figure 4: SVM confusion matrix for mixed-breed classification showing high diagonal accuracy with minimal off-diagonal misclassifications.