Compressed species classification models for biodiversity monitoring

Goldmann, Katriona, Strickson, Oliver, August, Tom A., Beuchert, Jonas, Carbone, Dylan, Iqbal, Mariya, Lawson, Jenna L., Skinner, Grace and Roy, David 2025. Compressed species classification models for biodiversity monitoring. Presented at: 29th International Conference on Theory and Practice of Digital Libraries, TPDL 2025, Tampere, Finland, 23-26 September 2025. Published in: Balke, Wolf-Tilo, Golub, Koraljka, Manolopoulos, Yannis, Stefanidis, Kostas, Zhang, Zheying, Aalberg, Trond and Manghi, Paolo eds. New Trends in Theory and Practice of Digital Libraries: TPDL 2025 Short Papers and Workshops, Tampere, Finland, September 23–26, 2025, Proceedings. Communications in Computer and Information Science Cham, Switzerland: Springer, 10.1007/978-3-032-06136-2_35

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Abstract

Scalable biodiversity monitoring remains a critical challenge for global conservation, particularly in ecologically rich but underrepresented regions with limited data infrastructure. The AMBER project (Automated Monitoring of Biodiversity using Edge and Remote Sensing) addresses this gap by integrating lightweight, compressed machine learning models with the AMI insect-monitoring system to enable on-device species identification. We focus on moth classification as a tractable use case and evaluate two end-to-end inference pipelines: a full-featured, server-based baseline and a compressed, edge-optimised alternative. To support field deployment on low-power devices, we apply quantisation, and model distillation techniques and evaluate trade-offs between full-featured server-based inference and resource-efficient edge deployment strategies. Our results show that compressed models retain strong classification performance while drastically reducing computation and bandwidth needs, enabling scalable, real-time monitoring in remote settings. This work lays the foundation for scalable, real-time ecological monitoring through trustworthy edge AI systems.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer
ISBN:	9783032061355
Last Modified:	07 Oct 2025 11:16
URI:	https://orca.cardiff.ac.uk/id/eprint/181480

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