Muzzle-Based Cattle Identification System Using Artificial Intelligence (AI)

• URL: http://arxiv.org/abs/2407.06096v2
• Date: Wed, 9 Oct 2024 04:17:12 GMT
• Title: Muzzle-Based Cattle Identification System Using Artificial Intelligence (AI)
• Authors: Hasan Zohirul Islam, Safayet Khan, Sanjib Kumar Paul, Sheikh Imtiaz Rahi, Fahim Hossain Sifat, Md. Mahadi Hasan Sany, Md. Shahjahan Ali Sarker, Tareq Anam, Ismail Hossain Polas,
• Abstract summary: The uniqueness of cattle muzzles has been scientifically established, which resembles human fingerprints. This is the premise that prompted us to develop a cattle identification system that extracts the uniqueness of cattle muzzles. Our system performs with an accuracy of $96.489%$, $F_1$ score of $97.334%$, and a true positive rate (tpr) of $87.993%$ at a remarkably low false positive rate (fpr) of $0.098%$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Absence of tamper-proof cattle identification technology was a significant problem preventing insurance companies from providing livestock insurance. This lack of technology had devastating financial consequences for marginal farmers as they did not have the opportunity to claim compensation for any unexpected events such as the accidental death of cattle in Bangladesh. Using machine learning and deep learning algorithms, we have solved the bottleneck of cattle identification by developing and introducing a muzzle-based cattle identification system. The uniqueness of cattle muzzles has been scientifically established, which resembles human fingerprints. This is the fundamental premise that prompted us to develop a cattle identification system that extracts the uniqueness of cattle muzzles. For this purpose, we collected 32,374 images from 826 cattle. Contrast-limited adaptive histogram equalization (CLAHE) with sharpening filters was applied in the preprocessing steps to remove noise from images. We used the YOLO algorithm for cattle muzzle detection in the image and the FaceNet architecture to learn unified embeddings from muzzle images using squared $L_2$ distances. Our system performs with an accuracy of $96.489\%$, $F_1$ score of $97.334\%$, and a true positive rate (tpr) of $87.993\%$ at a remarkably low false positive rate (fpr) of $0.098\%$. This reliable and efficient system for identifying cattle can significantly advance livestock insurance and precision farming.

Related papers

• MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm [2.9391768712283772]
  We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle. The dataset comprises 101, 329 images of 90 cows, plus the underlying original CCTV footage. We show that our framework enables fully automatic cattle identification, barring only the simple human verification of tracklet integrity.
  arXiv  Detail & Related papers  (2024-10-16T15:58:47Z)
• Cattle Identification Using Muzzle Images and Deep Learning Techniques [0.0]
  This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. From the experiments run, a maximum accuracy of 99.5% is achieved while using the wide ResNet50 model.
  arXiv  Detail & Related papers  (2023-11-14T13:25:41Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
  Training a high-performance deep neural network requires large amounts of data and computational resources. We propose a safe and robust backdoor-based watermark injection technique. We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
  arXiv  Detail & Related papers  (2023-09-04T19:58:35Z)
• Cattle Detection Occlusion Problem [0.0]
  The management of cattle over a huge area is still a challenging problem in the farming sector. With evolution in technology, Unmanned aerial vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative to manual animal censuses. This paper evaluated and compared the cutting-edge object detection algorithms, YOLOv7,RetinaNet with ResNet50 backbone, RetinaNet with EfficientNet and mask RCNN.
  arXiv  Detail & Related papers  (2022-12-21T23:59:13Z)
• A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions [3.758089106630537]
  This paper offers a systematic literature review ( SLR) of vision-based cattle identification. This SLR is to identify and analyse the research related to cattle identification using Machine Learning (ML) and Deep Learning (DL)
  arXiv  Detail & Related papers  (2022-10-13T14:10:12Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images [64.39996451133268]
  We present a semi-supervised approach for threat recognition which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. This can reduce threat detection false alarm rates by matching the statistics of extracted features from hand-collected backgrounds to real world data.
  arXiv  Detail & Related papers  (2020-10-02T21:05:13Z)
• Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning [8.784100314325395]
  Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques.
  arXiv  Detail & Related papers  (2020-06-16T14:41:55Z)
• Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review [79.49390241265337]
  Chalearn Face Anti-spoofing Attack Detection Challenge consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results.
  arXiv  Detail & Related papers  (2020-04-23T06:43:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.