Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA.

Area of Science:

• Environmental Science
• Microbiology
• Limnology

Background:

• Potentially toxic cyanobacteria in water bodies pose risks to human health.
• Accurate estimation of cyanobacteria abundance is crucial for water quality management.
• Existing manual methods for cyanobacteria quantification can be time-consuming and subjective.

Purpose of the Study:

• To compare manual and automated methods for estimating toxic filamentous cyanobacteria abundance and morphometrics.
• To assess the performance of the ACQUA (Automated Cyanobacterial Quantification Algorithm) software.
• To refine image processing techniques for improved cyanobacteria detection.

Main Methods:

• Data collection from three Italian volcanic lakes (Albano, Vico, Nemi).
• Application of the ACQUA algorithm for automated quantification and morphometric analysis.
• Image processing techniques including Sobel filtering for denoising, spline curves, and least squares method for filament parameterization.

Main Results:

• The ACQUA algorithm demonstrated efficiency in estimating abundance and morphometric characteristics of filamentous cyanobacteria.
• The study successfully set up and validated the denoising algorithm for improved accuracy.
• Developed and evaluated statistical tools and mathematical algorithms for software enhancement.

Conclusions:

• Automated quantification using ACQUA provides a reliable alternative to manual methods for assessing toxic cyanobacteria.
• The developed algorithm enhances the precision of water risk assessment for human consumption and recreational use.
• This study contributes to the development of advanced tools for effective water resource management.