arXiv:2606.00080v1 Announce Type: cross Abstract: Marine plankton underpin aquatic food webs and play a key role in global CO2 sequestration, making reliable species identification critical for understanding ocean health and climate feedbacks. Existing classification models perform well on individual collections but fail to generalize across instruments and environments due to isolated training datasets and inconsistent labels. To address this, we introduce Planktonzilla-17M, a unified dataset consolidating publicly available plankton image collections spanning thirteen imaging systems. It com
The increasing sophistication of AI models and the critical need for better environmental monitoring converge to enable the creation of comprehensive datasets like Planktonzilla-17M.
Reliable plankton identification is crucial for understanding ocean health, climate change, and global food webs, offering a vital feedback mechanism for planetary systems.
The unified Planktonzilla-17M dataset significantly improves the generalization capabilities of plankton classification models, overcoming previous limitations of isolated data.
- · Marine biologists
- · Climate researchers
- · Environmental monitoring agencies
- · AI developers in ecological applications
- · Legacy, siloed data collection methods
- · Organizations relying on disparate, non-standardized ecological datasets
Improved accuracy in automated plankton species identification across diverse imaging systems and environments.
Enhanced models will provide more granular and real-time insights into ocean health and climate feedback loops.
This could lead to more effective conservation strategies and climate change mitigation efforts based on a deeper understanding of marine ecosystems.
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