Efficient Coral Reef Mapping for Marine Ecosystem Conservation Using VGG16 Neural Network Model

Coral reefs are among the most diverse and functioning marine ecosystems while being threatened seriously by climate change, pollution, and human activities. Effective coral reef mapping and monitoring are critical for conservation efforts because they assist to understand ecosystem health, biodiversity, and habitat changes. The proposition aims at addressing the problem concerning poorly-mapped and classified coral reefs employing a VGG16 convolutional neural network to classify and map the features accurately. A modification for coral reef images was possible because of the use of VGG16 which was pre-trained on large-scale image databases, which made the classification reliable and effective. The combined approach makes good use of transfer learning incorporating fine-tuning on a specific coral reef dataset, hence enhancing the model's ability to recognize different coral types and stained areas. By preliminary benchmarks coral typing and mapping have been found to be accurate, making it an effect tool for biodiversity practitioners as well as marine scientists. This method has the potential to greatly assist in comprehensive evaluations by providing data that would inform a proactive strategy that aims at protecting and enhancing the coral reef ecosystem.