  Since coral reef ecosystems face threats from human activities and climate
change, coral conservation programs are implemented worldwide. Monitoring coral
health provides references for guiding conservation activities. However,
current labor-intensive methods result in a backlog of unsorted images,
highlighting the need for automated classification. Few studies have
simultaneously utilized accurate annotations along with updated algorithms and
datasets. This study aimed to create a dataset representing common coral
conditions and associated stressors in the Indo-Pacific. Concurrently, it
assessed existing classification algorithms and proposed a new multi-label
method for automatically detecting coral conditions and extracting ecological
information. A dataset containing over 20,000 high-resolution coral images of
different health conditions and stressors was constructed based on the field
survey. Seven representative deep learning architectures were tested on this
dataset, and their performance was quantitatively evaluated using the F1 metric
and the match ratio. Based on this evaluation, a new method utilizing the
ensemble learning approach was proposed. The proposed method accurately
classified coral conditions as healthy, compromised, dead, and rubble; it also
identified corresponding stressors, including competition, disease, predation,
and physical issues. This method can help develop the coral image archive,
guide conservation activities, and provide references for decision-making for
reef managers and conservationists. The proposed ensemble learning approach
outperforms others on the dataset, showing State-Of-The-Art (SOTA) performance.
Future research should improve its generalizability and accuracy to support
global coral conservation efforts.
