Applying Transfer Learning ResNet-50 for Tracking and Classification of A Coral Reef in Development The Mobile Application with Scrum Framework

Abstract

A Coral Reef has an important role in keeping a balanced ecosystem in the ocean. This research aims to develop a mobile application to track and classify to identify Coral Reefs with a presentation of information names of species, the type of classification of coral (hard or soft coral), accuracy, and area distribution that has been recorded before in certain areas. This research, utilized Flutter for deploying a mobile application, Deep Learning of Transfer Learning (TL) ResNet-50 for the classification of Coral Reefs, and the Google Cloud Platform used to deploy Rest API and deploy model TL ResNet-50 has been created, then the MongoDB database for storing data of the Coral Reef. A species of Coral Reef that could be classified to identify i.e. Acropora Clathrata, Acropora Florida, Cyphastrea Microphthalma, Diploastrea Heliopora, and Pachyseris Speciosa. A total dataset of a Coral Reef has 2197 images and is trained with TL ResNet-50 and it has resulted in an accuracy of 0.9594 or 96% for the classification of Corel Reef. We used the Scrum framework to produce a product in a short time and assist to manage process application development.