Segmentation of seagrass (Enhalus acoroides) using deep learning mask R-CNN algorithm

Abstract

Seagrass is an Angiosperms that live in shallow marine waters and estuaries. The method commonly used to identify seagrass is Seagrass-Watch which is done by sampling seagrass or by carrying a seagrass identification book. Technological developments in the era of the industrial revolution 4.0 made it possible to identify seagrass automatically. This research aims to apply the deep learning algorithm to detect seagrass recorded by underwater cameras. Enhalus acoroides seagrass species identification was carried out using a deep learning method with the mask region convolutional neural networks (Mask R-CNN) algorithm. The steps in the research procedure include collecting, labeling, training, testing models, and calculating the seagrass area. This study used 6000 epochs and got a measure of value generated by the model of 1.2. The Precision value, namely the model's ability to correctly classify objects, reached 98.19% and the model's ability to find all positive objects, based on system testing was able to perform recall is 95.04% and the F1 Score value of 96.58%. The results showed that the MASK R-CNN algorithm could detect and segment seagrass Enhalus acoroides.