Dairy DigiD ~ A Deep Learning-Based, Non-Invasive Biometric Identification System for Dairy Cattle Using Detectron2

Abstract

Precision livestock farming demands accurate and reliable individual animal identification for optimizing health monitoring, resource allocation, and overall herd productivity. Conventional identification methods--such as ear tagging and wearable sensors--are often invasive, potentially causing animal discomfort and affecting natural behaviors. To address these challenges, we introduce Dairy DigiD, a non-invasive biometric classification system for dairy cattle using facial image analysis. Leveraging deep learning and the Detectron2 framework, Dairy DigiD classifies cattle into four categories--young (<2 years), mature milking, pregnant, and old--using over 2,500 high-resolution facial images. A DenseNet121 model achieved a classification accuracy of 97%, showcasing its strong discriminative capability. Further, Detectron2 attained an average recall of 87% in category identification, an average precision of 96% in cattle detection, and an overall detection accuracy of 93%, demonstrating its robustness and adaptability to varied environmental conditions. Unlike traditional approaches that rely on fixed facial landmarks and can be sensitive to environmental variability, Dairy DigiDs flexible deep learning architecture enables stable and scalable facial feature extraction. This non-invasive method minimizes animal stress, enhances data integrity, and facilitates improved herd management and welfare monitoring. In comparison to existing convolutional neural network (CNN)-based strategies, Dairy DigiD exhibits superior adaptability to real-world farm conditions, including variable lighting and complex poses. This work highlights the potential of integrating advanced computer vision and deep learning methodologies into precision dairy farming, offering an ethically aligned, efficient, and scalable approach to modern livestock management.