mlsanityData to 14 Oct 2026

Automated Coral Spawn Monitoring for Reef Restoration: The Coral Spawn and Larvae Imaging Camera System (CSLICS)

arXiv 2509.17299 ↗First posted 22 Sep 2025cs.RO cs.CVcited by 0

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Abstract (arXiv)

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging Camera System (CSLICS), which uses low cost modular cameras and object detectors trained using human-in-the-loop labeling approaches for automated spawn counting in larval rearing tanks. This paper details the system engineering, dataset collection, and computer vision techniques to detect, classify and count coral spawn. Experimental results from mass spawning events demonstrate an F1 score of 82.4% for surface spawn detection at different embryogenesis stages, 65.3% F1 score for sub-surface spawn detection, and a saving of 5,720 hours of labor per spawning event compared to manual sampling methods at the same frequency. Comparison of manual counts with CSLICS monitoring during a mass coral spawning event on the Great Barrier Reef demonstrates CSLICS' accurate measurement of fertilization success and sub-surface spawn counts. These findings enhance the coral aquaculture process and enable upscaling of coral reef restoration efforts to address climate change threats facing ecosystems like the Great Barrier Reef.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 41 references that are in the map. First posted on arXiv 22 Sep 2025.

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5 of 41 references.

References · 5

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2019A deep active learning system for species identification and counting in camera trap imagescited by 44
  3. 2022Reconfigurable Robots for Scaling Reef Restorationcited by 5
  4. 2017Episode-Based Active Learning with Bayesian Neural Networkscited by 4
  5. 2025AI-driven Dispensing of Coral Reseeding Devices for Broad-scale Restoration of the Great Barrier Reefcited by 2

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