High speed water monitoring systems based on Digital Holographic Microscopy

Real-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring. Graphical abstract

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High speed water monitoring systems based on Digital Holographic Microscopy

Cited by 4 publications

References 22 publications

New Tools for the Monitoring of Cyanobacteria in Freshwater Ecosystems

New Tools for the Monitoring of Cyanobacteria in Freshwater Ecosystems

Label-Free Optical Biosensors for Monitoring Cellular Processes and Cytotoxic Agents at Interfaces Using Guided Modes and Advanced Phase-Contrast Imaging Techniques

A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration

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