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

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 multi-task architecture, using instance segmentation and keypoint detection across 30 anatomical landmarks (eyes, ears, muzzle) to refine facial localization and improve classification robustness. While DenseNet121 delivered strong baseline performance, its sensitivity to background noise limited generalizability. In contrast, Detectron2 demonstrated superior adaptability in uncontrolled farm environments, achieving classification accuracies between 93 and 98%. Its keypoint-driven strategy enabled robust feature localization and resilience to occlusions, lighting variations, and heterogeneous backgrounds. Cross-validation and perturbation-based explainability confirmed that biologically salient features guided classification, enhancing model transparency. By integrating animal-centric design with scalable AI, Dairy DigiD represents a significant advancement in automated livestock monitoring-offering an ethical, accurate, and practical alternative to traditional identification methods. The approach sets a precedent for responsible, data-driven decision-making in precision dairy management.