Insights on back marking for the automated identification of animals

TL;DR

This paper investigates how to design back marks on pigs for improved individual identification using machine learning, emphasizing the importance of unambiguous, robust mark design under various conditions to enhance automated monitoring accuracy.

Contribution

It provides guidelines for designing effective back marks for animals that are compatible with machine learning algorithms, based on analysis of a ResNet-50 model trained for pig identification.

Findings

Back marks must be unambiguous under motion blur, occlusion, and different angles.

Design should consider data augmentation effects like color, flip, and crop.

Effective mark design improves identification accuracy in automated systems.

Abstract

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 choices, even in controlled settings. Most importantly, the set of back marks must be designed such that each mark remains unambiguous under conditions of motion blur, diverse view angles and occlusions, caused by animal behaviour. Further, the back mark design must consider data augmentation strategies commonly employed during model training, like colour, flip and crop augmentations. The generated insights can support individual-level monitoring in future studies and real-world applications by optimizing back mark design.