Improving Cattle Identification Accuracy Using Attention-Based Convolutional Neural Networks

Automated livestock identification plays an important role in modern agricultural management and precision livestock farming. Conventional identification methods such as ear tags and RFID systems may suffer from loss, damage, or manual recording errors. As a result, computer vision-based biometric identification has emerged as a promising alternative.

This study proposes an improved cattle identification approach based on attention-enhanced convolutional neural networks. The proposed method integrates a channel attention mechanism into the convolutional feature extraction process to emphasize informative biometric patterns present in cattle muzzle images.

The model was trained and evaluated on a dataset consisting of multiple cattle individuals captured under real farm conditions. Experimental results demonstrate that the attention-based architecture improves feature representation and increases identification accuracy compared with conventional convolutional neural network models.

The proposed method contributes to the development of reliable automated livestock identification systems and provides a scalable solution for intelligent livestock monitoring applications.