Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition
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Arun Sanjel
Abstract
Accurate quick cattle identification is critical for successful livestock management, but traditional approaches frequently involve invasive procedures that raise ethical concerns and risk animal welfare. This chapter extends our previous research by introducing a deep learning-based methodology for noninvasive cattle identification through muzzle matching, utilizing a comprehensive dataset. Initially, our approach leveraged 4,923 cleaned and cropped muzzle images from 268 distinct cattle breeds, demonstrating exceptional accuracy with a training accuracy of 98.88% and a test accuracy of 100%. Additionally, we have extended our study to include another dataset consisting of 459 classes, achieving similar high performance. The model demonstrates outstanding accuracy, achieving 97.98% during training and 98.23% in testing across both datasets. Our approach avoids invasive practices and is highly adaptable, seamlessly integrating new animals into the system. Such flexibility ensures consistent performance across varied operational environments, making our model ideal for applications such as preventing insurance fraud and enhancing animal trading practices. Additionally, this chapter outlines key areas for future research, such as broadening the dataset to include more cattle breeds and muzzle types and exploring integration with other identification technologies.
Abstract
Accurate quick cattle identification is critical for successful livestock management, but traditional approaches frequently involve invasive procedures that raise ethical concerns and risk animal welfare. This chapter extends our previous research by introducing a deep learning-based methodology for noninvasive cattle identification through muzzle matching, utilizing a comprehensive dataset. Initially, our approach leveraged 4,923 cleaned and cropped muzzle images from 268 distinct cattle breeds, demonstrating exceptional accuracy with a training accuracy of 98.88% and a test accuracy of 100%. Additionally, we have extended our study to include another dataset consisting of 459 classes, achieving similar high performance. The model demonstrates outstanding accuracy, achieving 97.98% during training and 98.23% in testing across both datasets. Our approach avoids invasive practices and is highly adaptable, seamlessly integrating new animals into the system. Such flexibility ensures consistent performance across varied operational environments, making our model ideal for applications such as preventing insurance fraud and enhancing animal trading practices. Additionally, this chapter outlines key areas for future research, such as broadening the dataset to include more cattle breeds and muzzle types and exploring integration with other identification technologies.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
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Section: Image processing
- Chapter 1 Magnetic resonance image re-parameterization on real data 1
- Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
- Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
- Chapter 4 Image clustering enhanced with refined image classification 59
- Chapter 5 AI-powered framework for objective scoring of product design innovation 89
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Section: Computer vision
- Chapter 6 Image inpainting using GAN transformerbased model 111
- Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
- Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
- Chapter 9 Implementation of deep learning techniques on thermal image classification 173
- Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
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Section: Pattern recognition
- Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
- Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
- Index 273
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
-
Section: Image processing
- Chapter 1 Magnetic resonance image re-parameterization on real data 1
- Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
- Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
- Chapter 4 Image clustering enhanced with refined image classification 59
- Chapter 5 AI-powered framework for objective scoring of product design innovation 89
-
Section: Computer vision
- Chapter 6 Image inpainting using GAN transformerbased model 111
- Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
- Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
- Chapter 9 Implementation of deep learning techniques on thermal image classification 173
- Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
-
Section: Pattern recognition
- Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
- Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
- Index 273
