Absence of tamper-proof cattle identification technology was a significant problem preventing insurance companies from providing livestock insurance. This lack of technology had devastating financial consequences for marginal farmers as they did not have the opportunity to claim compensation for any unexpected events such as the accidental death of cattle in Bangladesh. Using machine learning and deep learning algorithms, we have solved the bottleneck of cattle identification by developing and introducing a muzzle-based cattle identification system. The uniqueness of cattle muzzles has been scientifically established, which resembles human fingerprints. This is the fundamental premise that prompted us to develop a cattle identification system that extracts the uniqueness of cattle muzzles. For this purpose, we collected 32,374 images from 826 cattle. Contrast-limited adaptive histogram equalization (CLAHE) with sharpening filters was applied in the preprocessing steps to remove noise from images. We used the YOLO algorithm for cattle muzzle detection in the image and the FaceNet architecture to learn unified embeddings from muzzle images using squared $L_2$ distances. Our system performs with an accuracy of $96.489\%$, $F_1$ score of $97.334\%$, and a true positive rate (tpr) of $87.993\%$ at a remarkably low false positive rate (fpr) of $0.098\%$. This reliable and efficient system for identifying cattle can significantly advance livestock insurance and precision farming.

翻译：缺乏防篡改的牛只识别技术一直是阻碍保险公司提供牲畜保险的关键问题。在孟加拉国，由于技术缺失，边缘农户在遭遇牛只意外死亡等突发事件时无法获得赔偿，造成了毁灭性的经济后果。通过应用机器学习和深度学习算法，我们开发并引入了一种基于鼻纹的牛只识别系统，从而解决了牛只识别的瓶颈问题。牛只鼻纹具有类似于人类指纹的独特性，这一特性已得到科学证实。基于这一基本前提，我们开发了能够提取牛只鼻纹独特特征的识别系统。为此，我们采集了826头牛共计32,374张图像。在预处理阶段，我们采用了对比度受限自适应直方图均衡化（CLAHE）结合锐化滤波器的方法以消除图像噪声。我们使用YOLO算法进行图像中的牛只鼻纹检测，并采用FaceNet架构，通过平方$L_2$距离从鼻纹图像中学习统一嵌入表示。本系统实现了$96.489\%$的准确率、$97.334\%$的$F_1$分数，并在极低的$0.098\%$误报率下达到了$87.993\%$的真阳性率。这一可靠高效的牛只识别系统有望显著推动牲畜保险和精准农业的发展。