Abstract
Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures.
Highlights
In grazing systems, livestock are primary consumers of biomass, whilst in intensive production systems such as feedlots, livestock require vast amounts of grains and roughage
We have proposed the YOLO-ResNet-50 muzzle biometric identification system as a novel deep learning modelling approach for the identification of individual cattle
The YOLO-ResNet-50 model addressed a major limitation of previous cattle identification systems by automating both the muzzle detection and individual identification steps within a single workflow
Summary
Livestock are primary consumers of biomass, whilst in intensive production systems such as feedlots, livestock require vast amounts of grains and roughage. A wide range of approaches, including ear tagging, ear tattooing, hot ironing, freeze branding, ID collars, microchipping and visual markers, such as paint, have been used to track and identify individual animals within herds [1]. Electronic identification tags and collars, such as National Livestock Identification Tags, require diligent record keeping and management with a certain proportion of tag failures incurred [8]. Other electronic approaches, such as microchipping, require subcutaneous injection [9] or injection of other electronic transponders or sensors into body parts such as the scutulum [10]. A major challenge to biometric algorithms is achieving the extremely high standards of accuracy required, for large and diverse herds of cattle breeds
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