Comparative study on supervised learning methods for identifying phytoplankton species

TL;DR

This paper compares various supervised learning methods for identifying phytoplankton species, proposing a new feature type and demonstrating that Random Forest achieves the highest accuracy of 98.24% in classification.

Contribution

It introduces a novel feature extraction method and evaluates multiple classifiers, identifying the most effective approach for phytoplankton species identification.

Findings

Random Forest with proposed features achieves 98.24% accuracy.

Proposed features outperform other feature types in classification.

The framework aids in environmental monitoring and water quality assessment.

Abstract

Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.