Three-dimensional cross-shelf zooplankton distributions off the Central Oregon Coast during anomalous oceanographic conditions

Briseño‐Avena, Christian; Schmid, Moritz S.; Swieca, Kelsey; Sponaugle, Su; Brodeur, Richard D.; Cowen, Robert K.

doi:10.1016/j.pocean.2020.102436

Cited by 26 publications

(24 citation statements)

References 62 publications

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“…For in-situ plankton imaging systems, classification algorithms do not account for shifting class distributions across samples, false positive rates acquired during model training, and because most plankton studies aim to estimate total group abundance across observations in space, or through time, the learning problem then becomes at the level of the sample, not the individual image [ 35 ]. Although any classifiers false positives can be corrected for (e.g., [ 86 ]), a generalizable classifier would contain robust sample-level error, not at the taxon level [ 35 ]. The features learned by CNNs for classification, similar to those described here, can be used for plankton quantification.…”

Section: Discussionmentioning

confidence: 99%

Plankton classification with high-throughput submersible holographic microscopy and transfer learning

et al. 2021

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Background Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes. Results Here we use a light and portable digital in-line holographic microscope (The HoloSea) with maximum optical resolution of 1.5 μm, intensity-based object detection through a volume, and four different pre-trained convolutional neural networks to classify > 3800 micro-mesoplankton (> 20 μm) images across 19 classes. The maximum classifier performance was quickly achieved for each convolutional neural network during training and reached F1-scores > 89%. Taking classification further, we show that off-the-shelf classifiers perform strongly across every decision threshold for ranking a majority of the plankton classes. Conclusion These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for high-throughput plankton monitoring.

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Section: Discussionmentioning

confidence: 99%

Plankton classification with high-throughput submersible holographic microscopy and transfer learning

et al. 2021

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“…In the case of YOLOv2, the recognition rate Figure 10i,j show the classification performances of YOLOv2 and the proposed method, respectively, for each frame. If the CNN or YOLO network recognizes the images in frame 1 to frame 8 as Common carp, and fails to recognize the images in frames 11,12,15,16,17,25, and 26 as fish, the fish species is recognized incorrectly. The proposed method has a low probability after frame 21, but is correctly recognized as Bluegill.…”

Section: Methodsmentioning

confidence: 99%

“…Many methods have been proposed based on the principles of CNN, and their performance has been demonstrated in various fields [7][8][9][10]. However, some studies in the field have been conducted using CNN [11][12][13][14]. The issue of image classification began in AlexNet [8] and further research has been carried out in GoogLeNet and VGGNet [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images

Park

Kang

2020

JMSE

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In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate their number. While YOLO shows excellent performance in object recognition, it recognizes objects by processing the images of each frame independently of each other. By accumulating the object classification results from the past frames to the current frame, we propose a method to accurately classify objects, and count their number in sequential video images. This has a high classification probability of 93.94% and 97.06% in the test videos of Bluegill and Largemouth bass, respectively. The proposed method shows very good classification performance in video images taken of the underwater environment.

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“…These processing techniques are all very similar, implementing some form of supervised learning-training an algorithm on a curated set of labeled images, evaluating using an independent subset, and then applying the algorithm incoming data. In the past 5 yr, virtually all plankton image classification schemes have adopted a flavor of deep learning (Luo et al 2018;Ellen et al 2019;Briseño-Avena et al 2020). These methods are quite accurate when evaluated using a random subset of the training data, typically achieving accuracies around 90%.…”

Section: Automated Analysis Comparisonmentioning

confidence: 99%

The Scripps Plankton Camera system: A framework and platform for in situ microscopy

Orenstein

Ratelle

Briseño‐Avena

et al. 2020

Limnology & Ocean Methods

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The large data sets provided by in situ optical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, an in situ microscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free-space, dark-field imaging microscope; (2) a server-based management system for data storage and analysis; and (3) a web-based user interface for real-time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine-learning-enabled studies illustrating its utility and efficacy.

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Three-dimensional cross-shelf zooplankton distributions off the Central Oregon Coast during anomalous oceanographic conditions

Cited by 26 publications

References 62 publications

Plankton classification with high-throughput submersible holographic microscopy and transfer learning

Plankton classification with high-throughput submersible holographic microscopy and transfer learning

A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images

The Scripps Plankton Camera system: A framework and platform for in situ microscopy

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