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The Coral Reef Health Assessment Study in the Gulf of Aqaba leverages advanced imaging and artificial intelligence (AI) technologies to evaluate coral ecosystems’ health, focusing on stress indicators like bleaching, tissue necrosis, and disease susceptibility. The study integrates Convolutional Neural Networks (CNNs) and decision tree (J48), optimizing the monitoring process with high-resolution underwater imagery and detailed environmental data analysis. Our results indicate a high accuracy in detecting coral health variations, with CNN achieving up to 95.6% accuracy in identifying healthy corals. The integrated approach of machine learning and meticulous data handling offers a promising avenue for enhancing the accuracy and efficiency of environmental monitoring and supporting robust conservation strategies.

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From Data to Action Toward Sustainable Marine Conservation: AI-Based Coral Health Assessment

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammad Wahsha,
  • Heider Wahsheh

摘要

The Coral Reef Health Assessment Study in the Gulf of Aqaba leverages advanced imaging and artificial intelligence (AI) technologies to evaluate coral ecosystems’ health, focusing on stress indicators like bleaching, tissue necrosis, and disease susceptibility. The study integrates Convolutional Neural Networks (CNNs) and decision tree (J48), optimizing the monitoring process with high-resolution underwater imagery and detailed environmental data analysis. Our results indicate a high accuracy in detecting coral health variations, with CNN achieving up to 95.6% accuracy in identifying healthy corals. The integrated approach of machine learning and meticulous data handling offers a promising avenue for enhancing the accuracy and efficiency of environmental monitoring and supporting robust conservation strategies.