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Deep learning computer vision system for estimating sheep age using teeth images
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AI
DOI：10.1080/09540091.2025.2506456.png)
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Abstract
En 中文
This study explores the use of deep learning neural networks and transfer learning to estimate the age of sheep from their dental images. This is an important aspect of agriculture for meat quality, animal welfare, breeding, and health management. Using cutting-edge techniques, MobileNet, ResNet50, and ResNet102, we compare two deep learning approaches: fine-tuning and feature extraction using the pre-trained version of these models as part of our investigation. We collected 540 images of sheep from nearby farms, concentrating on three age groups: young, middle-aged, and elderly, for the purpose of our study. With an interesting recognition accuracy of 96.9%, the experimental results demonstrate that ResNet102 is the best performer both when fine-tuned and when employing its deep features that are retrieved from its pre-trained version. These findings highlight how cutting-edge machine learning techniques have the potential to completely transform long-standing methods in the sheep sector and pave the way for developing a novel mobile application that improves economic outcomes and cultural conformity concerning sheep age recognition.
Keywords:
Deep learning
convolutional neural networks (CNNs)
meat quality
animal management
smart agriculture
sheep age estimation
Journal
IF:
3.4
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849
Citations:
1.5K
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