Deep learning method achieves 88% classification accuracy in cattle recognition, indicating non-invasive real-time monitoring benefits.
Accurate recognition of each cattle in group environments is essential for modern precision livestock management. This study proposed a multi-object cattle recognition method based on deep learning, enabling precise recognition in feeding passages. A dataset comprising facial images from 135 cattle was constructed, and a data augmentation strategy tailored to cattle facial characteristics was designed to enhance model generalisation. The YOLOv8n network was selected from a comparative experiment and further optimised. For multi-object bounding box regression, the standard CIoU loss was replaced by the MPDIoU loss, improving the mAP50 by 5.4% through optimised corner distance computation. In addition, a coordinate attention mechanism was embedded within the C2F module to strengthen the model’s spatial perception of key facial regions such as the eyes and nose, resulting in a 5.8% improvement in recognition precision. A comparative experiment between image-level segmentation and cattle-level segmentation datasets was carried out, and the proposed method was further validated on an untrained external test set collected from actual feeding Passages. The results demonstrate that, even under challenging conditions such as occlusion and illumination variation, the improved model achieved a classification accuracy of 88% while maintaining an average inference speed of 96.9 frames per second. This non-invasive, real-time recognition approach provides a novel solution for precision feeding in group-housed environments and offers valuable insights for improving the efficiency of livestock monitoring and feeding management systems.
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Wang et al. (2025) studied this question.