Cow Face Detection for Precision Livestock Management using YOLOv8

Abstract

Precision livestock management is transforming traditional agricultural practices by boosting productivity, increasing yield, and automating tasks, all while reducing labor requirements and minimizing errors. Conventional methods for animal recognition are often unreliable, which has led to a growing preference for using cameras to identify animals, monitor their health, manage data, and maintain cattle records. However, small-scale farms with limited livestock, such as cows and goats, frequently face overfitting problems in traditional machine learning models due to insufficient training data. Identifying individual cows based on facial features becomes more effective after detecting the cow’s face. This study addresses these challenges by fine-tuning YOLOv8, a pretrained model, using a mix of self-captured images and publicly available datasets to detect cow faces in complex environments. Integrating publicly available data and leveraging a pretrained COCO model has significantly improved the model’s ability to generalize and accurately detect cow faces. YOLOv8, equipped with the COCO pretrained model, successfully detects nearly all types of cow faces, which can then be used for individual cow classification. This approach enhances cow recognition accuracy, contributing to more efficient farm management applications.