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Overview of Algae

Overview of Algae

The kingdom Archaeplastida encompasses red and green algae, along with land plants. Unlike other protists with chloroplasts that arose through secondary endosymbiosis, only red and green algae originated from primary endosymbiotic events. This diverse group of eukaryotic organisms contains chlorophyll and performs oxygenic photosynthesis.Algae exist in various forms, from large brown kelp in coastal waters to green scum in puddles and stains on rocks or soil. Some species are responsible for...

Green Algae

Green Algae

Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...

Red Algae

Red Algae

Red algae, also known as rhodophytes, are primarily found in marine environments, though some species inhabit freshwater and terrestrial ecosystems. These organisms exist in both unicellular and multicellular forms, with some multicellular varieties reaching macroscopic sizes.As phototrophic organisms, red algae contain chlorophyll a; however, their chloroplasts lack chlorophyll b. Instead, they possess phycobiliproteins, which serve as major light-harvesting pigments, similar to those found in...

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Updated: Aug 15, 2025

Analysis of Fatty Acid Content and Composition in Microalgae

Analysis of Fatty Acid Content and Composition in Microalgae

Published on: October 1, 2013

Trends in digital image processing of isolated microalgae by incorporating classification algorithm.

Jun Wei Roy Chong¹, Kuan Shiong Khoo², Kit Wayne Chew³

¹Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia.

Biotechnology Advances

|January 7, 2023

Summary

This summary is machine-generated.

Digital microscopy and machine learning offer advanced methods for identifying microalgae species, overcoming limitations of traditional techniques. This approach enables reliable recognition, detection, and enumeration for environmental and industrial applications.

Keywords:

Identification Image processing Machine learning Microalgae Microscopy

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Last Updated: Aug 15, 2025

Analysis of Fatty Acid Content and Composition in Microalgae

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Published on: October 1, 2013

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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Published on: February 25, 2021

Area of Science:

Marine Biology
Biotechnology
Image Analysis

Background:

Harmful algae blooms pose risks to aquatic ecosystems and human health.
Microalgae are valuable for bioactive compounds and industrial applications.
Traditional microalgae identification methods (DNA analysis, electron microscopy) are costly and time-consuming.

Purpose of the Study:

To review innovations in digital microscopy for microalgae species identification.
To explore the integration of hardware and software for enhanced microalgae analysis.
To address challenges in developing robust, automated microalgae classification tools.

Main Methods:

Image acquisition and processing to enhance microalgae image quality.
Feature extraction and selection for accurate species identification.
Machine learning (ML) and deep learning (DL) algorithms (ANNs, SVMs, CNNs) for image classification.

Main Results:

Digital microscopy combined with ML/DL provides reliable recognition, detection, and enumeration of microalgae species.
Image pre-processing techniques effectively remove artifacts and noise.
AI-driven classification offers a more accurate and efficient alternative to traditional methods.

Conclusions:

Digital microscopy integrated with AI presents a promising future for automated microalgae identification.
This technology can support monitoring of harmful algae blooms and industrial utilization of microalgae.
Further development of robust digital classification tools is essential.