Automated Pattern Recognition of Phytoplankton – Procedure and Results

Schlimpert, Olaf; Uhlmann, Dirk; Schüller, Martina; Höhne, E.

doi:10.1002/iroh.19800650311

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…To paint a picture of the history of machine learning for plankton image classification, we conducted a systematic literature review that yielded 174 publications (see Supplemental Appendix 1 and the accompanying Supplemental Data containing the references in a commonly readable bibliographic file format). The first paper that used machine learning to classify plankton images dates from 1980 and performed pattern extraction on digital microscopy images to classify five genera of phytoplankton (Schlimpert et al 1980). The literature started to build in the mid-1990s (Figure 4) but grew at a rate similar to that of the field of oceanography as a whole until 2012 (i.e., the yearly number of papers standardized by the total number in the field is flat).…”

Section: A History Of Machine Learning Approachesmentioning

confidence: 99%

Machine Learning for the Study of Plankton and Marine Snow from Images

Irisson

Ayata

Lindsay

et al. 2022

Annu. Rev. Mar. Sci.

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Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: A History Of Machine Learning Approachesmentioning

confidence: 99%

Machine Learning for the Study of Plankton and Marine Snow from Images

Irisson

Ayata

Lindsay

et al. 2022

Annu. Rev. Mar. Sci.

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“…The microscopic images were converted into two-dimensional spectral frequency and classified by pattern recognizing algorithm. 54 Later, with preprocessed microscopic images (with Fourier transformation and edge detection), two neural networks and two classical statistical techniques identified 23 dinoflagellates from the images. Among them, a radial basis network outperformed others with 83% accuracy (human 85% accuracy).…”

Section: Biological Oceanographymentioning

confidence: 99%

“…The classification of phytoplankton using the ML started in the early 1990s. The microscopic images were converted into two-dimensional spectral frequency and classified by pattern recognizing algorithm . Later, with preprocessed microscopic images (with Fourier transformation and edge detection), two neural networks and two classical statistical techniques identified 23 dinoflagellates from the images.…”

Section: Biological Oceanographymentioning

confidence: 99%

Applications of Machine Learning in Chemical and Biological Oceanography

et al. 2023

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Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

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“…Machine learning is one of these techniques, in which a computer system learns patterns from a training dataset and then subsequently can find these same patterns in another independent test dataset. The first study using machine learning to classify plankton images dates back to 1980, for which pattern extraction on digital microscopy images to classify five genera of phytoplankton was performed ( Schlimpert et al 1980 ). Since then, machine-learning techniques for image and video annotation of plankton have been drastically improved and a significant increase in published papers was observed after 2012 (reviewed by Irisson et al In press ).…”

Section: Introductionmentioning

confidence: 99%

Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning

Verhaegen¹,

Cimoli²,

Lindsay³

2021

BDJ

693

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Southern Ocean ecosystems are currently experiencing increased environmental changes and anthropogenic pressures, urging scientists to report on their biodiversity and biogeography. Two major taxonomically diverse and trophically important gelatinous zooplankton groups that have, however, stayed largely understudied until now are the cnidarian jellyfish and ctenophores. This data scarcity is predominantly due to many of these fragile, soft-bodied organisms being easily fragmented and/or destroyed with traditional net sampling methods. Progress in alternative survey methods including, for instance, optics-based methods is slowly starting to overcome these obstacles. As video annotation by human observers is both time-consuming and financially costly, machine-learning techniques should be developed for the analysis of in situ /in aqua image-based datasets. This requires taxonomically accurate training sets for correct species identification and the present paper is the first to provide such data. In this study, we twice conducted three week-long in situ optics-based surveys of jellyfish and ctenophores found under the ice in the McMurdo Sound, Antarctica. Our study constitutes the first optics-based survey of gelatinous zooplankton in the Ross Sea and the first study to use in situ / in aqua observations to describe taxonomic and some trophic and behavioural characteristics of gelatinous zooplankton from the Southern Ocean. Despite the small geographic and temporal scales of our study, we provided new undescribed morphological traits for all observed gelatinous zooplankton species (eight cnidarian and four ctenophore species). Three ctenophores and one leptomedusa likely represent undescribed species. Furthermore, along with the photography and videography, we prepared a Common Objects in Context (COCO) dataset, so that this study is the first to provide a taxonomist-ratified image training set for future machine-learning algorithm development concerning Southern Ocean gelatinous zooplankton species.

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Automated Pattern Recognition of Phytoplankton – Procedure and Results

Cited by 6 publications

References 1 publication

Machine Learning for the Study of Plankton and Marine Snow from Images

Machine Learning for the Study of Plankton and Marine Snow from Images

Applications of Machine Learning in Chemical and Biological Oceanography

Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning

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