The feasibility of automated identification of six algae types using neural networks and fluorescence-based spectral-morphological features

TL;DR

This study demonstrates that neural networks trained on fluorescence spectral-morphological features can accurately and automatically identify six algae types, significantly outperforming traditional human taxonomy accuracy.

Contribution

The paper introduces a novel approach combining fluorescence spectral-morphological features with neural networks for automated algae identification, achieving high accuracy.

Findings

Neural network models achieved over 95% accuracy in algae identification.

Fluorescence-based features significantly improved classification accuracy over morphological features.

Automated identification accuracy surpasses human taxonomists, indicating practical viability.

Abstract

Harmful algae blooms (HABs), which produce lethal toxins, are a growing global concern since they negatively affect the quality of drinking water and have major negative impact on wildlife, the fishing industry, as well as tourism and recreational water use. In this study, we investigate the feasibility of leveraging machine learning and fluorescence-based spectral-morphological features to enable the identification of six different algae types in an automated fashion. More specifically, a custom multi-band fluorescence imaging microscope is used to capture fluorescence imaging data of a water sample at six different excitation wavelengths ranging from 405 nm - 530 nm. A number of morphological and spectral fluorescence features are then extracted from the isolated micro-organism imaging data, and used to train neural network classification models designed for the purpose of identification of the six algae types given an isolated micro-organism. Experimental results using three different neural network classification models showed that the use of either fluorescence-based spectral features or fluorescence-based spectral-morphological features to train neural network classification models led to statistically significant improvements in identification accuracy when compared to the use of morphological features (with average identification accuracies of 95.7%+/-3.5% and 96.1%+/-1.5%, respectively). These preliminary results are quite promising, given that the identification accuracy of human taxonomists are typically between the range of 67% and 83%, and thus illustrates the feasibility of leveraging machine learning and fluorescence-based spectral-morphological features as a viable method for automated identification of different algae types.