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AI-based automated monitoring of the invasive pearl oyster (Pinctada radiata) in the Aegean Sea using underwater surveys

delete2026-08-03
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OA
AI
D
D. Pafras
D
D. Politikos
A
A. Theocharis
D
D. Klaoudatos *
DOI：10.1016/j.ecoinf.2026.103968delete
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Abstract

Abstract

En 中文
• Underwater videos reveal invasive oyster populations without disturbing the seabed. • Deep learning turns raw video into automated counts of sessile marine invaders. • Tracking individuals through video delivers diver-level accuracy in abundance estimates. • Oyster size is estimated from imagery and biomass is inferred using a field-calibrated length–weight relationship.
Keywords:
Invasive species monitoring
Underwater video surveys
Deep learning ecology
Automated image analysis
Sessile bivalves
Mediterranean Sea

Journal

Ecological Informatics cover
Ecological Informatics
IF:
7.3
Papers:
3.7K
Citations:
1.3W

Organization

U
University of Thessaly
Scholars:
7.4K
Papers: 5.9K
Citations: 5.7K
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