Coral-CRCA: Un algoritmo de automatización de cartas de referencia de color para la visualización y evaluación de la...

Coral reefs are among the most diverse and productive marine ecosystems, providing food security and livelihoods for millions of people, especially in coastal and rural areas where small-scale reef fisheries are a main source of fish. Rising sea temperatures and pollution are causing widespread bleaching, driven by the loss of algal symbionts, and accelerating reef degradation. Underwater imaging paired with color-based health charts provides a non-invasive method for monitoring bleaching. However, current approaches require extensive manual annotation and are limited by underwater image noise such as blur and color casts. Existing AI-based monitoring approaches are often semi-autonomous and lack fine-grained bleaching localization capabilities. In this paper, we propose Coral Color-Reference Chart Automation (Coral-CRCA), a multi-stage algorithm that replicates the visual assessment process used by marine biologists to fully automate coral bleaching evaluation with color-reference charts. Initially, the pipeline incorporates a preprocessing image denoising module to improve robustness to underwater image distortions. The method then segments the coral region, isolates chart quadrants, and assigns each coral pixel to the closest reference grade by color similarity, generating pixel-level bleaching visualizations and reporting bleaching percentage. We evaluate each stage of the pipeline through comparative analyses and assess the complete system on 3400 expert-annotated, field-collected coral images from the Arabian/Persian Gulf. The method achieves a mean absolute error (MAE) of 19.17% in bleaching percentage estimation and a binary classification accuracy (bleached/healthy) of 96.12%, matching expert-level performance on this dataset. The source codes are available on this link https://github.com/MahmoudElMezain/Coral-CRCA_Color-Reference-Chart-Automation-Algorithm.