Performance of machine learning methods for cattle identification and recognition from retinal images

Abstract

Animal identification is a critical issue in terms of security, traceability, and animal health, especially in large-scale livestock enterprises. Traditional methods (such as ear tags and branding) both negatively affect animal welfare and may lead to security vulnerabilities. This study aims to develop a biometric system based on retinal vascular patterns for the identification and recognition of cattle. This system aims to provide a safer and animal welfare-friendly alternative by using image processing techniques instead of traditional device-based methods. In the study, preprocessing, segmentation, feature extraction, and performance evaluation steps were applied for the biometric identification and recognition process using retinal images taken from both eyes. Techniques such as green channel extraction, contrast-limited adaptive histogram equalization, morphological operations, noise filtering, and threshold determination were used in the preprocessing stage. Fuzzy C-means, K-means, and Level-set methods were applied for segmentation, and feature extraction was performed using SIFT, SURF, BRISK, FAST, and HARRIS methods. At the end of the study, the highest accuracy rate was obtained as 95.6% for identification and 87.9% for recognition. In addition, the obtained dataset was shared publicly, thus creating a reusable resource that researchers from different disciplines can use. It was concluded that this study made a significant contribution to the field of biometric-based animal identification and recognition and offered a practically usable solution in terms of animal welfare and safety.