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

2025-09-21

AI Summary

Low-throughput, labor-intensive manual counting of coral propagules (eggs, embryos, larvae) hinders large-scale reef restoration. This study introduces a lightweight, human-in-the-loop object detection framework integrating a low-cost modular underwater imaging system, enabling the first continuous, automated identification and classification of multi-stage coral propagules on and beneath the substrate surface. Leveraging a high-quality annotated dataset and model architecture optimization, the system achieves F1 scores of 82.4% (surface) and 65.3% (subsurface) during the Great Barrier Reef spawning event—reducing labor requirements by 5,720 person-hours per spawning cycle versus manual methods—and supports precise quantification of critical health metrics such as fertilization rate. By overcoming fundamental bottlenecks of traditional manual sampling, this system establishes a scalable, intelligent monitoring paradigm for industrial-scale coral nurseries.

Technology Category

• Computer Vision: Object Detection & Categorization
• Humans and AI: Crowd Sourcing and Human Computation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automating labor-intensive coral spawn counting for reef restoration
• Monitoring coral larval health and fertilization success accurately
• Addressing critical bottleneck in coral aquaculture production pipeline

Innovation

• Low-cost modular cameras for automated monitoring
• Human-in-the-loop object detection for spawn counting
• Computer vision techniques for detection and classification