فهرست نشریات علمی دسترسی آزاد ایران

BACKGROUND AND OBJECTIVES : Coral reefs provide vital ecological and economic benefits but are increasingly threatened by climate change, pollution, and anthropogenic stressors that drive bleaching and degradation. Effective monitoring is essential, yet conventional field assessments remain labor-intensive, time-consuming, and expert-dependent. The study objectives were to develop a convolutional neural network-based framework for automated classification of reef coral morphotypes and coral health status from underwater photographs; evaluate three transfer learning architectures, dense convolutional network with 169 layers, residual network with 152 layers, and visual geometry group network with 19 layers, under limited and imbalanced training data; and deploy the best-performing model for automated reef health monitoring across five stations at Kelapa Dua Island, Indonesia. METHODS : Reef coral morphotypes were categorized as boulder, branching, and table, while health status was classified as healthy, bleached, or partially bleached using the Coral Health Chart as a standardized reference. Three convolutional neural networks architectures pre-trained on ImageNet were fine-tuned on datasets of 663 morphotype images and 624 health status images, with class-weighted loss applied to address pronounced imbalance in the health dataset. FINDINGS : dense convolutional network with 169 layers achieved the highest overall accuracy, reaching 93 percent for morphotype classification and 82 percent for health status assessment, substantially outperforming residual network with 152 layers 152 (66 percent and 64 percent) and Visual geometry group network with 19 layers (42 percent and 64 percent). Per-class analysis revealed that the health classification task is significantly affected by class imbalance, with the partially bleached category dominating the dataset (55 percent of health samples); the F1-score for the healthy class reached only 0.44, highlighting systematic model bias. CONCLUSION : These findings demonstrate both the promise and the current limitations of deep learning for automated coral reef monitoring, and underscore the need for larger, class-balanced, and station-independent datasets for future model development. The dense convolutional network with 169 model was deployed successfully at five monitoring stations at Kelapa Dua Island, where partially bleached coral conditions predominated, consistent with elevated sea surface temperatures and suppressed salinity at the time of the survey.