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.

翻译：精准的牲畜个体识别是现代养殖业的基石：它支撑着健康监测、育种计划和生产力追踪。然而，常见的猪只识别方法，如耳标和微芯片，通常存在可靠性低、成本高、仅针对纯种猪等问题，因此对小规模养殖户而言并不实用。为弥补这一不足，我们提出了一种非侵入式生物特征识别方法，该方法利用了耳部静脉模式的独特性。为此，我们使用标准智能手机和简易背光装置，采集了20头杂交猪（长白猪×皮特兰猪和杜洛克猪×皮特兰猪）的800张耳部图像。我们开发了一个多阶段计算机视觉流程，用于增强静脉可见性、提取结构与空间特征并生成生物特征签名。随后使用机器学习模型对这些特征进行分类。支持向量机（SVM）取得了最高的准确率：在杂交猪群中识别猪只的精确度达到98.12%。从图像处理到分类的整个过程平均耗时8.3秒，证明了其在农场实时部署的可行性。我们相信，通过用永久性的生物标记替代易损的物理标识符，该系统为养殖户提供了一种经济高效且无应激的动物识别方法。更广泛地说，研究结果证实了耳部静脉生物识别技术在牲畜管理数字化方面的实用性，强化了其将精准农业的优势扩展到资源受限农业社区的潜力。