Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents
arXiv cs.AI·Jiaxing Li, Hao Fang, Chi Xu, Miao Zhang, Jiangchuan Liu, William I. Atlas, Katrina M. Connors, Mark A. Spoljaric
1d agoQuick Take
The study proposes knowledge-adaptive edge agents for efficient ecological monitoring amidst biodiversity loss.
Key Points
- Addresses challenges of manual biodiversity surveys.
- Introduces a visual encoder with a dynamic knowledge base.
- Promotes ethical AI through collaboration with biologists and Indigenous communities.
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 10 pages |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16671 [cs.AI] |
| (or arXiv:2605.16671v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16671 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaxing Li [view email]
[v1]
Fri, 15 May 2026 22:12:02 UTC (5,967 KB)
— Originally published at arxiv.org
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