Insights on back marking for the automated identification of animals
To date, there is little research on how to design back marks to best support individual-level monitoring of uniform looking species like pigs. With the recent surge of machine learning-based monitoring solutions, there is a particular need for guidelines on the design of marks that can be effectively recognised by such algorithms. This study provides valuable insights on effective back mark design, based on the analysis of a machine learning model, trained to distinguish pigs via their back marks. Specifically, a neural network of type ResNet-50 was trained to classify ten pigs with unique back marks. The analysis of the model's predictions highlights the significance of certain design c...
| Authors | David Brunner, Marie Bordes, Elisabeth Mayrhuber et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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