ORCID
- Kyran P. Graves: 0000-0002-9312-8905
- Kerry L. Howell: 0000-0003-3359-1778
Abstract
The integration of artificial intelligence (AI), particularly convolutional neural networks (CNNs), into ecological research presents new opportunities for the automated analysis of image-based data. This study explores the practical application of CNNs for ecological image analysis by trialling annotation to different levels of taxonomic classification to determine their impact on model performance. We systematically compare various annotation strategies, evaluating their effects on the accuracy of CNNs in ecological contexts; as well as considering the feasibility of manually annotating training data to different levels. We demonstrate that variation in annotations groupings (animal, phylum or morphology) has little impact on model performance, despite large differences in class numbers. Consequently, the decision for annotators should hinge on whether to invest effort in detailed annotation at the beginning of a project or to perform finer sorting of model predictions at the end. These findings provide practical guidance for optimizing the workflow in AI-driven ecological studies, offering flexibility without compromising model performance.
Publication Date
2025-04-12
Publication Title
Ecological Informatics
Volume
88
ISSN
1574-9541
Keywords
Artificial intelligence, Computer vision, Convolutional neural networks, Ecological image analysis, Image annotation, Underwater imagery
Recommended Citation
Bridges, A., Cross, E., Graves, K., Piechaud, N., Raymont, A., & Howell, K. (2025) 'Practical application of artificial intelligence for ecological image analysis: Trialling different levels of taxonomic classification to promote convolutional neural network performance', Ecological Informatics, 88. Available at: 10.1016/j.ecoinf.2025.103146
