Cephalopod species identification using integrated analysis of machine learning and deep learning approaches.

ABSTRACT:

Background

Despite the high commercial fisheries value and ecological importance as prey item for higher marine predators, very limited taxonomic work has been done on cephalopods in Malaysia. Due to the soft-bodied nature of cephalopods, the identification of cephalopod species based on the beak hard parts can be more reliable and useful than conventional body morphology. Since the traditional method for species classification was time-consuming, this study aimed to develop an automated identification model that can identify cephalopod species based on beak images.

Methods

A total of 174 samples of seven cephalopod species were collected from the west coast of Peninsular Malaysia. Both upper and lower beaks were extracted from the samples and the left lateral views of upper

SUBMITTER: Tan HY 

PROVIDER: S-EPMC8359798 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature