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Related Experiment Video

Updated: May 30, 2025

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

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Multi-modal learning-based algae phyla identification using image and particle modalities.

Do Hyuck Kwon¹, Min Jun Lee², Heewon Jeong³

¹Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

|January 26, 2025

Summary

This summary is machine-generated.

An artificial intelligence (AI) multi-modal approach effectively identifies algal phyla in water treatment. This AI method integrates algal images and particle properties, improving water quality and supply safety.

Keywords:

Algae Deep Learning Multi-modal Multi-modal algae identifier

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Published on: June 28, 2017

Area of Science:

Environmental Science
Water Treatment Technologies
Artificial Intelligence in Environmental Monitoring

Background:

Algal blooms in freshwater systems are intensified by urbanization and climate change, complicating water treatment.
Effective identification of dominant algal species is crucial for maintaining water quality and ensuring a safe drinking water supply.
Traditional algae identification methods are often slow and require specialized expertise.

Purpose of the Study:

To introduce and evaluate an artificial intelligence (AI)-based multi-modal approach for enhanced algal identification in water treatment.
To integrate algal images and particle properties for robust and reliable classification of algal phyla.
To assess the performance and explainability of the AI model in identifying key algal species.

Main Methods:

Utilized a multi-modal learning approach combining algal images and particle properties obtained via FlowCam.
Employed early, late, and hybrid fusion techniques to integrate the multi-modal dataset.
Applied explainable AI methods (SHAP, Grad-CAM) to understand feature contributions.

Main Results:

The multi-modal algae identifier with late fusion achieved high F1 scores: 0.91 for training and 0.88 for testing.
Both image and particle data modalities demonstrated significant potential as complementary components in deep learning algorithms.
The AI approach proved robust and reliable for classifying major algal phyla like Anabaena, Microcystis, and Synedra.

Conclusions:

The developed AI multi-modal approach offers a significant advancement for rapid and accurate algal identification in water treatment.
Integrating diverse data modalities enhances the reliability of algal classification, contributing to improved water quality management.
This AI-driven method supports the goal of a safe and clean water supply by enabling effective monitoring of dominant algal species.