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Planktonic organisms including phyto-, zoo-, and mixoplankton are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand these changes. Advances in imaging technology have enabled novel possibilities to study plankton populations, but the manual classification of images is time consuming and expert-based, making such an approach unsuitable for large-scale application and urging for automatic solutions for the analysis, especially recognizing the plankton species from images. Despite the extensive research done on automatic plankton recognition, the latest cutting-edge methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for operational use. Then, we provide a detailed description of solutions found in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. Many important challenges remain unsolved including the following: (1) the domain shift between the datasets hindering the development of an imaging instrument independent plankton recognition system, (2) the difficulty to identify and process the images of previously unseen classes and non-plankton particles, and (3) the uncertainty in expert annotations that affects the training of the machine learning models. To build harmonized instrument and location agnostic methods for operational purposes these challenges should be addressed in future research. [ABSTRACT FROM AUTHOR]

This article reports the development of an underwater imaging system and its trial on a moored surface buoy for in situ plankton monitoring of coastal waters. The imager features shadowless white light illumination by an orthogonal lamellar lighting design, resulting in high-quality underwater darkfield color imaging of planktonic particles in the size range of ∼200 μm to 40 mm and effective reduction of zooplankton phototaxis. Through raft and buoy trials, 46 804 plankton and suspending particle images have been annotated through a human–machine mutually assisted effort into a data set with 90 categories. In the meanwhile, a deep learning model based on a triclassification VGGNet-11 and multiclassification ResNet-18 convolutional neuron networks in a two-staged hierarchy has also been trained and developed. The model has been applied with human supervision to semiautomatically analyze a total of 1 545 187 images obtained from a buoy trial for six months from late spring to early winter of 2020. The high temporal resolution results well documented the variation of the mesoplankton community structure in two time series of 38 days in summer and 54 days in autumn of the target sea region. In addition, the dominant species in the trial period and a zooplankton outbreak that had threatened the safety of the nearby nuclear power plants were quantitatively analyzed. The system is expected to become a new entry into the toolkit for marine surface buoy platforms to upscale their capabilities for more comprehensive in situ plankton monitoring. [ABSTRACT FROM AUTHOR]

Phytoplankton blooms, including harmful algal blooms (HABs), have serious impacts on ecosystems, public health, and productivity activities. Rapid detection and monitoring of marine microalgae are important in predicting and managing HABs. We developed a toolkit, the Suitcase Lab, to detect harmful algae species in the field. We demonstrated the Suitcase Lab's capabilities for sampling, filtration, DNA extraction, and loop-mediated isothermal amplification (LAMP) detection in cultured Alexandrium catenella cells as well as Chilean coastal waters from four sites: Repollal, Isla García, Puerto Montt, and Metri. A LAMP assay using the Suitcase Lab in the field confirmed microscopic observations of A. catenella in samples from Repollal and Isla García. The Suitcase Lab allowed the rapid detection of A. catenella, within 2 h from the time of sampling, even at a single cell per milliliter concentrations, demonstrating its usefulness for quick and qualitative on-site diagnosis of target toxic algae species. This method is applicable not only to detecting harmful algae but also to other field studies that seek a rapid molecular diagnostic test. [ABSTRACT FROM AUTHOR]

Plankton size spectra monitoring is crucial for managing and conserving aquatic ecosystems. Thus, we develop an in situ size spectra monitoring system to obtain the size spectra of plankton and the information of their living status underwater. The system consists of an imaging unit and an information processing unit. The imaging part applies a darkfield illumination to enhance the image contrast. Three lenses with different magnifications are alternated by a motor automatically to capture sizes of plankton from $3\,\mu$ m to $3\,\text{mm}$. Moreover, the system can analyze the captured images in real time using the proposed multitask size spectra convolutional neural network, obtaining size spectra and density distribution of plankton simultaneously. Field test confirms that our system performs well both in imaging and information processing. Furthermore, the system can provide the living behavior of plankton, thereby helping biologists to study the aquatic ecosystem effectively and precisely. [ABSTRACT FROM AUTHOR]

• An in situ holographic microscope developed to monitor Karenia brevis. • K. brevis automatically classified from images using a convolutional neural network. • Results showed good agreement with manual counts and flow cytometry. • Towing system paired with sensor allows for spatial mapping of plankton/blooms. • Promising method for adaptation in long term plankton/bloom monitoring networks. Karenia brevis blooms, also known as red tide, are a recurring problem in the coastal Gulf of Mexico. These blooms have the capacity to inflict substantial damage to human and animal health as well as local economies. Thus, monitoring and detection of K. brevis blooms at all life stages and cell concentrations is essential for ensuring public safety. Current K. brevis monitoring methods have several limitations, including size resolution limits and concentration ranges, limited capacity for spatial and temporal profiling, and/or small sample volume processing. Here, a novel monitoring method wherein an autonomous digital holographic imaging microscope (AUTOHOLO), that overcomes these limitations and can characterize K. brevis concentrations in situ, is presented. Using the AUTOHOLO, in situ field measurements were conducted in the coastal Gulf of Mexico during an active K. brevis bloom over the 2020–21 winter season. Surface and sub-surface water samples collected during these field studies were also analyzed in the lab using benchtop holographic imaging and flow cytometry for validation. A convolutional neural network was trained for automated classification of K. brevis at all concentration ranges. The network was validated with manual counts and flow cytometry, yielding a 90% accuracy across diverse datasets with varying K. brevis concentrations. The usefulness of pairing the AUTOHOLO with a towing system was also demonstrated for characterizing particle abundance over large spatial distances, which could potentially facilitate characterization of spatial distributions of K. brevis during bloom events. Future applications of the AUTOHOLO can include integration into existing HAB monitoring networks to enhance detection capabilities for K. brevis in aquatic environments around the world. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we succeeded in developing a loop-mediated isothermal amplification (LAMP) method that enables sensitive and specific detection of the toxic marine dinoflagellates Alexandrium tamarense and Alexandrium catenella from single cells of both laboratory cultures and naturally blooming cells within 25min, by monitoring the turbidimeter from the start of the LAMP reaction. The fluorescence intensity was strong enough to allow discrimination between positive and negative results by naked eye under a UV lamp, even in amplified samples from a single cell, by using the LAMP method. Unambiguous detection by naked eye was possible even in half the volume of LAMP cocktail recommended by the manufacturer, suggesting the potential to significantly reduce the cost of Alexandrium monitoring. Therefore, we can conclude that this method is one of the most convenient, sensitive, and cost-effective molecular tools for Alexandrium monitoring. [Copyright &y& Elsevier]