Individual identification of Holstein dairy cows based on detecting and matching feature points in body images

Zhao, Kaixuan; Jin, Xin; Ji, Jiangtao; Wang, Jun; Ma, Hao; Zhu, Xuefeng

doi:10.1016/j.biosystemseng.2019.03.004

Cited by 41 publications

(21 citation statements)

References 25 publications

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“…Facial recognition technology has also been adapted and applied to several animal species, including chimpanzees [17] and lemurs [18]. Research on the biometric identification of livestock spans cattle [19][20][21], sheep [22,23], horses [24,25] and pigs [26,27]. Biometric recognition of cattle is of particular interest given the major role of the beef and dairy industries.…”

Section: Introductionmentioning

confidence: 99%

“…Biometric recognition of cattle is of particular interest given the major role of the beef and dairy industries. Cattle biometric recognition approaches include coat pattern analysis [21,28,29], facial recognition [30,31], gait analysis [32] and muzzle print pattern analysis [33][34][35]. The various approaches offer several advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Machine vision systems have the advantages of no contact, low cost, and low stress [5]. However, the lack of stable and reliable image-based individual identification methods and technologies seriously restricts the promotion and use of these machine vision systems [6].…”

Section: Introductionmentioning

confidence: 99%

“…In the test, 90.55% of the images were correctly identified. Zhao et al [6] proposed a cow recognition method based on template matching. The feature template library was generated by extracting the trunk image features of all cows, and individual cows were identified by matching their trunk image features with the features in the template library.…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

Zhao

Zhang

2021

Sensors

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In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

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“…Then, the iris image was normalized, and the local and global characteristics of the milk cattle were extracted to complete the individual identification of dairy cows based on the iris images. In [22], a vision system to extract body images and identify cows was proposed. The FAST, SIFT and FLANN methods were used for feature extraction, descriptor, and matching.…”

Section: Introductionmentioning

confidence: 99%

Cow Rump Identification Based on Lightweight Convolutional Neural Networks

et al. 2021

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Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device.

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Individual identification of Holstein dairy cows based on detecting and matching feature points in body images

Cited by 41 publications

References 25 publications

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

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

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

Cow Rump Identification Based on Lightweight Convolutional Neural Networks

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