Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

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.

Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

“…A best model classification accuracy of 98.7% was achieved in this study ( Table 3 and Table 5 ) and was comparable to that of other deep learning studies for cattle muzzle recognition, in which the accuracy was 98.9% [ 27 , 28 ] and 99.1% [ 29 ]. Despite the discrepancies of cattle breeds, rearing environments, data acquisition conditions, and network architecture, all these studies achieved the desired accuracy (>90%), which again proves the empowering object recognition ability of deep learning and suggests a suitable application of the deep learning technique for individual cattle identification.…”

“…Deep learning models can capture spatial and temporal dependencies of images/videos through the use of shared-weight filters and can be trained end-to-end without strenuous hand-crafted design of feature extractors [ 26 ], empowering the models to adaptively discover the underlying class-specific patterns and the most discriminative features automatically. Kumar et al [ 27 ], Bello et al [ 28 ], and Shojaeipour et al [ 29 ] tried deep learning models (e.g., convolutional neural network, deep belief neural network, You Only Look Once, and residual network) in large sets (over 2900 images in total) of dairy and beef cattle muzzle images and obtained great accuracies of over 98.9%. The US beef cattle industry is quite unique from the dairy sector, in terms of both animal genetics and housing environment [ 3 ], which may result in different bioinformatic markers between dairy and beef cattle that influence model classification performance.…”

“…The US beef cattle industry is quite unique from the dairy sector, in terms of both animal genetics and housing environment [ 3 ], which may result in different bioinformatic markers between dairy and beef cattle that influence model classification performance. Shojaeipour et al [ 29 ] investigated the muzzle pattern of beef cattle, but the cattle were constrained in a crush restraint (i.e., hydraulic squeeze chute) with their heads placed in head scoops for data collection, which may cause extra distress to the cattle. Moreover, except for the study of Shojaeipour et al [ 29 ], the muzzle image datasets were not publicly available in most studies, limiting the progress of developing models tailored for beef cattle applications.…”

“…Shojaeipour et al [ 29 ] investigated the muzzle pattern of beef cattle, but the cattle were constrained in a crush restraint (i.e., hydraulic squeeze chute) with their heads placed in head scoops for data collection, which may cause extra distress to the cattle. Moreover, except for the study of Shojaeipour et al [ 29 ], the muzzle image datasets were not publicly available in most studies, limiting the progress of developing models tailored for beef cattle applications.…”

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

“…In our opinion, such adaptive systems have a serious drawback, they cannot be customized to the individual characteristics of each root crop. In digital agriculture [10,11], computer vision systems are used to quickly detect and count plants [12][13][14][15], to determine their ripeness and diseases [16][17][18][19][20], as part of systems to protect against weeds and pests [21,22], to determine the position of cattle [23]. In recent years publications have shown that the problem of identifying diseased or mechanically damaged fetuses on transportation systems such as conveyor belts, drums, turbines and etc.…”