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Relevant bibliographies by topics / Phytoplankton recognition

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Academic literature on the topic 'Phytoplankton recognition'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Phytoplankton recognition"

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Shapiro, LP, L. Campbell, and EM Haugen. "Immunochemical recognition of phytoplankton species." Marine Ecology Progress Series 57 (1989): 219–24. http://dx.doi.org/10.3354/meps057219.

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McCall, Helen, Isabel Bravo, J. Alistair Lindley, and Beatriz Reguera. "Phytoplankton recognition using parametric discriminants." Journal of Plankton Research 18, no. 3 (1996): 393–410. http://dx.doi.org/10.1093/plankt/18.3.393.

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Li, Qiong, Xin Sun, Junyu Dong, et al. "Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning." ICES Journal of Marine Science 77, no. 4 (2019): 1427–39. http://dx.doi.org/10.1093/icesjms/fsz171.

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Abstract Phytoplankton plays an important role in marine ecological environment and aquaculture. However, the recognition and detection of phytoplankton rely on manual operations. As the foundation of achieving intelligence and releasing human labour, a phytoplankton microscopic image dataset PMID2019 for phytoplankton automated detection is presented. The PMID2019 dataset contains 10 819 phytoplankton microscopic images of 24 different categories. We leverage microscopes to collect images of phytoplankton in the laboratory environment. Each object in the images is manually labelled with a bou

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Abonyi, Andras, Jean-Pierre Descy, Gábor Borics, and Evangelia Smeti. "From historical backgrounds towards the functional classification of river phytoplankton sensu Colin S. Reynolds: what future merits the approach may hold?" Hydrobiologia 848, no. 1 (2020): 131–42. http://dx.doi.org/10.1007/s10750-020-04300-3.

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AbstractRiver phytoplankton has been studied to understand its occurrence and composition since the end of the nineteenth century. Later, pioneers addressed mechanisms that affected river phytoplankton by “origin of plankton”, “turbulent mixing”, “flow heterogeneity”, “paradox of potamoplankton maintenance” and “dead zones” as keywords along the twentieth century. A major shift came with the recognition that characteristic units in phytoplankton compositions could be linked to specific set of environmental conditions, known as the “Phytoplankton Functional Group concept” sensu Reynolds. The FG

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Bautista-Regil, Jesús, Alberto J. Sánchez, Miguel Ángel Salcedo, Bertha Olivia Arredondo-Vega, and Violeta Ruiz-Carrera. "Lipid Prospection Based on the Cellular Size of Phytoplankton Communities from Tropical Freshwater Ecosystems: A Systematic Literature Review." Water 15, no. 21 (2023): 3774. http://dx.doi.org/10.3390/w15213774.

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Eutrophication-resistant phytoplankton communities in freshwater ecosystems have a novel lipid potential to contribute to the development of tropical regions. The question that arises due to the unsustainability of their eutrophicated waters is how the recognition of the lipids of the resident phytoplankton progresses. Our aim was to provide an overview of the pico-, nano- and micro-cellular lipids of phytoplankton with a focus on eutrophic tropical freshwater ecosystems. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, global and Latin American

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Chen, Hai Yan, Ni Yang, and Feng Xia Han. "The Discrimination of Fluorescence Spectra of Phytoplankton for Environment Protection Based on the PCA and SVM." Advanced Materials Research 600 (November 2012): 63–66. http://dx.doi.org/10.4028/www.scientific.net/amr.600.63.

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Issues of environmental protection and sustainable development are gaining an increasing importance in everyday life, and nowhere is this more than in the field of Materials Science and Engineering. The alga is the most common phytoplankton, identifying them can estimate the community structure and distribute status of ecosystem in the sea area and realize the inspecting and comprehensive father of sea. In this paper, the three dimension fluorescence spectra and principal component analysis method is combined to identify the ocean phytoplankton. Aiming at the east China sea, adopt the selectio

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Lysenko, A. V., M. S. Oznobikhin, E. A. Kireev, K. S. Dubrova, and S. S. Vorobyeva. "Identification of Baikal phytoplankton inferred from computer vision methods and machine learning." Limnology and Freshwater Biology, no. 3 (2021): 1143–46. http://dx.doi.org/10.31951/2658-3518-2021-a-3-1143.

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Abstract. This study discusses the problem of phytoplankton classification using computer vision methods and convolutional neural networks. We created a system for automatic object recognition consisting of two parts: analysis and primary processing of phytoplankton images and development of the neural network based on the obtained information about the images. We developed software that can detect particular objects in images from a light microscope. We trained a convolutional neural network in transfer learning and determined optimal parameters of this neural network and the optimal size of

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Amadei, Martínez Luz, Jonas Mortelmans, Nick Dillen, Elisabeth Debusschere, and Klaas Deneudt. "LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea." Biodiversity Data Journal 8 (December 16, 2020): e57236. https://doi.org/10.3897/BDJ.8.e57236.

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This paper describes a phytoplankton data series generated through systematic observations in the Belgian Part of the North Sea (BPNS). Phytoplankton samples are collected during multidisciplinary sampling campaigns, visiting nine nearshore stations with monthly frequency, and an additional eight offshore stations on a seasonal basis.The data series contain taxon specific phytoplankton densities determined by analysis with the Flow Cytometer And Microscope (FlowCAM®) and associated image based classification. The classification is performed by two separate semi-automated classification systems

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Durham, Bryndan P., Stephen P. Dearth, Shalabh Sharma, et al. "Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system." Environmental Microbiology 19, no. 9 (2017): 3500–3513. http://dx.doi.org/10.1111/1462-2920.13834.

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Rodenacker, Karsten, Burkhard Hense, Uta Jütting, and Peter Gais. "Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation." Microscopy Research and Technique 69, no. 9 (2006): 708–20. http://dx.doi.org/10.1002/jemt.20338.

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Dissertations / Theses on the topic "Phytoplankton recognition"

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Ebert, Kerstin [Verfasser]. "Exceptional phytoplankton bloom recognition from visible spectral satellite radiometry data / Kerstin Ebert." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/1023750376/34.

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Cheng, Tsung-chin, and 鄭宗欽. "Automatic recognition and numerating of phytoplankton with microscopic imaging." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62220480058973707638.

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碩士<br>國立臺灣大學<br>環境工程學研究所<br>96<br>The aim of this research is to develop a system for automatic monitoring phytoplankton in real water bodies. The system includes sample injector, digital image capture system , digital image process, and pattern recognition. The first part of the system is the sample injector including the flowing cell the pump pumping sample to the focal point of the microscope intermittently. The second part of the system is image capture system in which CCD camera captures images and the acquired images were processed for pattern recognition based on the sample from Zui-Yue

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Tsai, Yu-chia, and 蔡郁佳. "The development of numerating and recognition system for phytoplankton." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/40969160209473058747.

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碩士<br>臺灣大學<br>環境工程學研究所<br>95<br>The aim of this research is to develop a system for monitoring the types and abundance of phytoplanktons in water. Flow cytometry, digital image capturing, digital image processing, and pattern recognition(PR) were integrated in the system. Four major sets of researches were performed in this study. The first set of the research workes were the design and fabrication of the flowing cell. This was followed by image capturing by digital camera through microscope. The third set of research was image pre-processing, then the images obtained from the secong step were

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Book chapters on the topic "Phytoplankton recognition"

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Caillault, Émilie, Pierre-Alexandre Hébert, and Guillaume Wacquet. "Dissimilarity-Based Classification of Multidimensional Signals by Conjoint Elastic Matching: Application to Phytoplanktonic Species Recognition." In Engineering Applications of Neural Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03969-0_15.

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Conference papers on the topic "Phytoplankton recognition"

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Kang, Lin, Yuanhao Gong, Chenhui Yang, Jinfei Luo, Qiaoqi Luo, and Yahui Gao. "Marine Phytoplankton Recognition Using Hybrid Classification Methods." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5517750.

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Alarcon, Vladimir J., John van der Zwaag, and Robert Moorhead. "Estimation of Estuary Phytoplankton using a Web-based Tool for Visualization of Hyper-spectral Images." In 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06). IEEE, 2006. http://dx.doi.org/10.1109/aipr.2006.22.

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A.E., Oudijk, Johansen T. A., Johnsen G., and Alver M. O. "Simulating Hyperspectral Datacubes for Ocean Color: A Foundation for Phytoplankton Recognition in Spectral Signatures." In OCEANS 2023 - MTS/IEEE U.S. Gulf Coast. IEEE, 2023. http://dx.doi.org/10.23919/oceans52994.2023.10337371.

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Culverhouse, P. F. "Expert and machine discrimination of marine flora: a comparison of recognition accuracy of field-collected phytoplankton." In International Conference on Visual Information Engineering (VIE 2003). Ideas, Applications, Experience. IEE, 2003. http://dx.doi.org/10.1049/cp:20030516.

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