Coral reefs are vital yet fragile ecosystems currently facing severe threats due to climate change, pollution, and unsustainable human activities. Immediate and innovative restoration strategies are essential to preserve their ecological significance. This research explores the integration of advanced machine learning techniques, including YOLOv8, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, to enhance coral reef monitoring and restoration efforts.
The study utilizes YOLOv8, a real-time object detection model, to identify destructive species such as the Crown-of-Thorns Starfish (COTS), facilitating timely intervention and localized ecological protection. Simultaneously, RNNs are employed to analyze time-series data for predicting species interactions and coral health trends in response to environmental changes. To capture longer temporal dependencies, LSTM networks are incorporated to track and interpret long-term patterns in reef health influenced by climate variability and anthropogenic stressors.
This integrated approach aims to optimize the identification of suitable restoration sites, improve deployment strategies, and enable continuous monitoring of reef health. By leveraging real-time detection and predictive modeling, the research offers a scalable and data-driven framework to support resilient coral reef ecosystems. The insights generated aim to aid marine biologists, conservationists, and policymakers in making informed decisions, ultimately contributing to the sustainability and longevity of reef restoration efforts.