Dairy DigiD: a keypoint-based deep learning system for classifying dairy cattle by physiological and reproductive status.

ABSTRACT: Precision livestock farming increasingly relies on non-invasive, high-fidelity systems capable of monitoring cattle with minimal disruption to behavior or welfare. Conventional identification methods, such as ear tags and wearable sensors, often compromise animal comfort and produce inconsistent data under real-world farm conditions. This study introduces Dairy DigiD, a deep learning-based biometric classification framework that categorizes dairy cattle into four physiologically defineda groups-young, mature milking, pregnant, and dry cows-using high-resolution facial images. The system combines two complementary approaches: a DenseNet121 model for full-image classification, offering global visual context, and Detectron2 for fine-grained facial analysis. Dairy DigiD leverages Detectron2's

SUBMITTER: Mahato S 

PROVIDER: S-EPMC12411556 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature