Assessment of imaging-in-flow system (FlowCAM) for systematic ballast water management

Lee

et al. 2019

Water

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Section: Introductionmentioning

confidence: 99%

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

Lee

et al. 2019

Water

“…However, this method does not provide detailed insights on the sample taxonomic composition. FlowCAM theoretically allows combining flow cytometry capabilities and microscopy accuracy, but significant methodological improvements are still required (See et al 2005;Álvarez et al 2011Romero-Martínez et al 2017). Recently, molecular techniques (metabarcoding) have been developed for inventorying taxa in environmental samples, and they can efficiently identify rare, unknown, or hidden microorganisms (Grzebyk et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of FluoroProbe® performance for the phytoplankton-based assessment of the ecological status of Mediterranean coastal lagoons

Garrido

Cecchi

Bakhouche

et al. 2019

Environ Monit Assess

The European Water Framework Directive and several other legislations worldwide have selected phytoplankton for monitoring the ecological status of surface waters. This assessment is a complicated task in coastal lagoons due to their intrinsic variability, prompting moves to use real-time measurements. Here, we tested the ability of the submersible spectrofluorometer FluoroProbe® to accurately estimate the phytoplankton biomass and to efficiently discriminate spectral groups in Mediterranean coastal lagoons, by using sub-surface water samples (n = 107) collected at Biguglia lagoon (Corsica) in different environmental situations (salinity and trophic state) from March 2012 to December 2014. We compared the estimates of biomass and phytoplankton group composition obtained with the FluoroProbe® (in situ and lab measurements) with the spectrofluorimetrically measured biomass and HPLC-derived quantifications of pigment concentrations. FluoroProbe® provided good estimates of the total phytoplankton biomass (particularly, the lab measurements). The FluoroProbe® data were significantly correlated with the HPLC results, except for the in situ measurements of very weak concentrations of blue-green and red algae. Our findings indicate that factory-calibrated FluoroProbe® is an efficient and easy-to-use real-time phytoplankton monitoring tool in coastal lagoons, especially as an early warning system for the detection of potentially harmful algal blooms. Practical instructions dedicated to non-specialist field operators are provided. A simple and efficient method for discarding in situ measurement outliers is also proposed.

“…The FlowCAM (Flow Cytometer and Microscope, Fluid Imaging Technologies, Yarmouth, ME, USA) is a potential alternative to the conventional cell counting method [21] that has been widely used for the identification, classification, and biomass measurement of algae in water bodies by providing unbiased results within a relatively short time (e.g., within 5 min) [22][23][24]. Rowe et al captured images of individual colonies of Microcystis sp.…”

Section: Introductionmentioning

confidence: 99%

A novel method for cell counting of Microcystis colonies in water resources using a digital imaging flow cytometer and microscope

Environmental Engineering Research

Kim

et al. 2018

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.