| dc.description.abstract | This research outlines the integration of image
processing with an Aquabot, an underwater rover to
monitor the growth of corals. Designed system composed
of underwater vehicle, a floating station for communication
and coral monitoring unit. This monitoring unit can
classify the coral type and also to identify available
diseases on the coral. Communication occurs through a
combination of wired and cloud methods. The coral
diseasedetection unit was evaluated on a data set of 121
images containing 489 instances of coral disease and the
coral varieties detection model was evaluated on a dataset
of 124 images containing 492 instances of coral varieties.
These images are taken from corals reefs and coral
nurseries around Sri Lanka. In both cases the models
identified the diseases and varieties correctly with overall
confidence levels of 33.1% and 59.6% respectively. The two
models achieved box precision of 0.346 and 0.679, the box
recallsof 0.331 and 0.558, the mean average precision
(mAP50) of 0.295 and
0.596 and the mean average precision at IoUthresholds of
0.5 to 0.95 (mAP50-95) of 0.188 and 0.425 respectively.
The results of the evaluations show that both models are
effective for respective tasks. This research introduces a
combination of AquaBot and an image processing system,
which is capable of real time monitoring and identification
of corals and its diseases. | en_US |
