Microalgae identification: Future of image processing and digital algorithm.

Area of Science:

• Marine Biology
• Biotechnology
• Computational Science

Background:

• Microalgae identification is vital for ecological monitoring (e.g., harmful algae blooms) and biotechnological applications (e.g., bioactive compound discovery).
• Traditional identification methods can be time-consuming and require specialized expertise.
• Emerging technologies aim for faster, more accurate, and cost-effective identification solutions.

Purpose of the Study:

• To review the significance of microalgae identification in scientific research and commercial sectors.
• To highlight the advancements and potential of deep learning (DL) methods in microalgae species recognition.
• To explore the integration of DL with image processing techniques for enhanced classification accuracy.

Main Methods:

• Review of existing literature on microalgae identification techniques.
• Focus on machine learning algorithms, particularly deep learning, for image classification.
• Discussion of essential image pre-processing, feature extraction, and selection methods.

Main Results:

• Deep learning models demonstrate significant improvements in efficiency and accuracy for microalgae species identification.
• DL facilitates the development of applications for recognizing both toxic and valuable microalgae strains.
• The integration of DL with image analysis offers a state-of-the-art approach to microalgae recognition.

Conclusions:

• Deep learning represents a powerful tool for advancing microalgae identification, offering rapid, accurate, and reliable solutions.
• Further development of DL models and image capturing technologies will enhance microalgae recognition capabilities.
• This approach supports ecological management and the sustainable utilization of microalgae resources.