Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

TL;DR

This paper introduces a knowledge-adaptive edge AI architecture for ecological monitoring, enabling sustainable, resource-efficient biodiversity assessment in remote environments by separating perception from reasoning.

Contribution

It proposes a novel architecture that separates visual perception from reasoning using a dynamic knowledge base, enhancing sustainability and ethical collaboration in ecological AI applications.

Findings

Achieved resource-efficient biodiversity monitoring in remote settings.

Enabled knowledge sustainability by structuring expert insights.

Facilitated ethical AI co-development with biologists and Indigenous communities.

Abstract

Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.