Game, C. A. | Search bioRxiv

bioRxiv2025

Deep Blueprint: A Literature Review and Guide to Automated Image Classification for Ecologists

O_LIDeep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. C_LIO_LITo address this, we created a practical guide and review, focused on one of the fundamental tasks in automated image analysis: image classification. Our approach integrates commonly used software and highlights key steps - from image acquisition to annotated, model-ready datasets, to training, evaluation and deployment. It is modular and supported by a flexible code base (Python & R) and Graphical User Interfaces (GUIs), enabling adaptation to different models and ecological objectives. The goal is to empower ecologists to confidently incorporate computer vision into their research. C_LIO_LIWe illustrate this approach, using an open-source ROV dataset from the Norwegian Sea, featuring deep-sea biotopes defined by multivariate clusters of depth, substrate type, and associated species. To balance accessibility for users alongside performance, we focused on CNN models from the Ultralytics ML Platform (YOLO V.8 and V.11), comparing the full suite of architectures that range in complexity and efficiency. C_LIO_LICross-validation revealed high overall performances and that larger, more complex models are not always superior, with YOLO V.8m best (Accuracy = [~]0.98). Notably, high performances were achieved despite labels being based on both visual and external environmental predictors, suggesting visual features alone were sufficient for classification in this dataset. We highlight that the decision to deploy a model must be made in light of the studys objectives, with domain-based reasoning and experience guiding every stage of implementation. C_LIO_LIThis work offers a practical blueprint for implementing DL in ecological research, promoting broader adoption and supports reproducibility and more efficient, standardized, and sustainable monitoring; in this case of deep-sea biotopes, which is essential for marine spatial planning. C_LI

ecology↗

bioRxiv2019

A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses

Video and image data are regularly used in the field of benthic ecology to document biodiversity. However, their use is subject to a number of challenges, principally the identification of taxa within the images without associated physical specimens. The challenge of applying traditional taxonomic keys to the identification of fauna from images has led to the development of personal, group, or institution level reference image catalogues of operational taxonomic units (OTUs) or morphospecies. Lack of standardisation among these reference catalogues has led to problems with observer bias and the inability to combine datasets across studies. In addition, lack of a common reference standard is stifling efforts in the application of artificial intelligence to taxon identification. Using the North Atlantic deep sea as a case study, we propose a database structure to facilitate standardisation of morphospecies image catalogues between research groups and support future use in multiple front-end applications. We also propose a framework for coordination of international efforts to develop reference guides for the identification of marine species from images. The proposed structure follows the Darwin Core standard to allow integration with existing databases. We suggest a management framework where high-level taxonomic groups are curated by a regional team, consisting of both end users and taxonomic experts. We identify a mechanism by which overall quality of data within a common reference guide could be raised over the next decade. Finally, we discuss the role of a common reference standard in advancing marine ecology and supporting sustainable use of this ecosystem.

ecology↗