RESUMO
In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous fish, endangered sea cows, Dugong dugon, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on lightweight DeepSeagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classifies the seagrasses quickly and with lesser parameters. The DeepSeagrass dataset is used to test LWDS's applicability.
