Relevant bibliographies by topics / Plankton images

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Academic literature on the topic 'Plankton images'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Plankton images"

1

Prakasa, E., A. Rachman, D. R. Noerdjito, and R. Wardoyo. "Development of segmentation algorithm for determining planktonic objects from microscopic images." IOP Conference Series: Earth and Environmental Science 944, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1755-1315/944/1/012025.

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Abstract Plankton are free-floating organisms that live, grow, and move along with the ocean currents. This free-floating organism plays important roles as primary producers, they serve as a link to energy transfer, and a factor that regulates the biogeochemical cycles. Indonesia, with almost 60% of its territory covered by the ocean, harbours a wide variety of planktonic species. However, one of the issues within usual planktonic studies is the lack of a fast and accurate method for identifying and classifying the plankton type. Thus, the computer vision methods on microscopic images were proposed to deal with the problem. The classification follows two main steps, detecting plankton location and followed by plankton differentiation. The segmentation algorithm is required to limit the determination area. The present study describes the segmentation methods on fifteen plankton types. The U-Net based architecture was implemented to segment the plankton texture from other objects. The segmentation result was also compared with the manual assessment to compute the performance parameters. The accuracy, 0.970±0.025, gives the highest value whereas the smallest value is found in the precision parameter, 0.761±0.156.
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2

Campbell, R. W., P. L. Roberts, and J. Jaffe. "The Prince William Sound Plankton Camera: a profiling in situ observatory of plankton and particulates." ICES Journal of Marine Science 77, no. 4 (2020): 1440–55. http://dx.doi.org/10.1093/icesjms/fsaa029.

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Abstract A novel plankton imager was developed and deployed aboard a profiling mooring in Prince William Sound in 2016–2018. The imager consisted of a 12-MP camera and a 0.137× telecentric lens, along with darkfield illumination produced by an in-line ring/condenser lens system. Just under 2.5 × 106 images were collected during 3 years of deployments. A subset of almost 2 × 104 images was manually identified into 43 unique classes, and a hybrid convolutional neural network classifier was developed and trained to identify the images. Classification accuracy varied among the different classes, and applying thresholds to the output of the neural network (interpretable as probabilities or classifier confidence), improved classification accuracy in non-ambiguous groups to between 80% and 100%.
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3

Shahani, Kamran, Hong Song, Syed Raza Mehdi, et al. "Design and Testing of an Underwater Microscope with Variable Objective Lens for the Study of Benthic Communities." Journal of Marine Science and Application 20, no. 1 (2021): 170–78. http://dx.doi.org/10.1007/s11804-020-00185-9.

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AbstractMonitoring the ecology and physiology of corals, sediments, planktons, and microplastic at a suitable spatial resolution is of great importance in oceanic scientific research. To meet this requirement, an underwater microscope with an electrically controlled variable lens was designed and tested. The captured microscopic images of corals, sediments, planktons, and microplastic revealed their physical, biological, and morphological characteristics. Further studies of the images also revealed the growth, degradation, and bleaching patterns of corals; the presence of plankton communities; and the types of microplastics. The imaging performance is majorly influenced by the choice of lenses, camera selection, and lighting method. Image dehazing, global saturation masks, and image histograms were used to extract the image features. Fundamental experimental proof was obtained with micro-scale images of corals, sediments, planktons, and microplastic at different magnifications. The designed underwater microscope can provide relevant new insights into the observation and detection of the future conditions of aquatic ecosystems.
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4

Karmini, Mimin, and H. Yuniarto. "BIOSTRATIGRAFI FORAMINIFERA KUARTER PADA BOR INTI MD 982152 DAN 982155 DARI SAMUDRA HINDIA." JURNAL GEOLOGI KELAUTAN 11, no. 2 (2016): 55. http://dx.doi.org/10.32693/jgk.11.2.2013.231.

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Dari bor inti pada EKSPEDISI IMAGES, di Samudra Hindia, telah diteliti sebanyak 21 percontoh sedimen dari lokasi MD 982152, dan 29 buah dari lokasi MD 982155 untuk kepentingan biostratigrafi berdasarkan analisis foraminifera plankton dalam interval 1,5 meter. 
 Pada kedua penampang bor inti tersebut hanya dijumpai satu zona foraminifera plankton Kuarter, yaitu Zona Globorotalia truncatulinoides. Untuk MD 982152, zona ini bisa dibagi ke dalam dua subzona, yakni Subzona-subzona Globorotalia crassaformis hessi dan Globigerinella calida, sedangkan untuk MD 982155, zona tersebut bisa dibagi lagi ke dalam tiga subzona, yakni Subzona-subzona Globorotalia crassaformis hessi Globigerinella calida, dan Beella digitata. 
 Kejadian yang signifikan di kedua penampang itu adalah Datum Pemunculan Pertama dari Globigerinella calida dan Pemunculan Akhir dari Globorotalia crassaformis hessi. Pada MD 982155, dijumpai Pemunculan Pertama dari Beella digitata.
 
 Kata kunci: foraminifera plankton, Kuarter, biostratigrafi, Samudra Hindia.
 
 
 From IMAGES Expedition in Indian Ocean, 21 samples from MD 982152, and 29 samples from MD 982155 had been studied for the purpose of biostratigraphy based on planktonic foraminifera within 1,5 meter interval. 
 In both sections, only one Quaternary zone is found, namely Globorotalia truncatulinoides Zone. For MD 982152, that zone can be subdivided into two interval subzones e.g. Globorotalia crassaformis hessi and Globigerinella calida calida. However, in MD 982155 Globorotalia truncatulinoides Zone can be subdivided into three subzones namely, Globorotalia crassaformis hessi, Globigerinella calida calida, and Beella digitata Subzones. 
 The planktonic foraminifera event revealed in both sections are the First Appearance Datum (FAD) of Globigerinella calida calida and the Last Appearance (LAD) of Globorotalia crassaformis hessi. In MD 982155 the FAD of Beella digitata is found. 
 
 Keywords: planktonic foraminifera, Quaternary, biostratigraphy, Indian Ocean.
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5

Luo, T., K. Kramer, D. B. Goldgof, et al. "Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 4 (2004): 1753–62. http://dx.doi.org/10.1109/tsmcb.2004.830340.

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6

Cheng, Xuemin, Yong Ren, Kaichang Cheng, Jie Cao, and Qun Hao. "Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye." Sensors 20, no. 9 (2020): 2592. http://dx.doi.org/10.3390/s20092592.

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In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.
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7

Schröder, Simon-Martin, Rainer Kiko, and Reinhard Koch. "MorphoCluster: Efficient Annotation of Plankton Images by Clustering." Sensors 20, no. 11 (2020): 3060. http://dx.doi.org/10.3390/s20113060.

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In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.
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8

Ohman, Mark D. "A sea of tentacles: optically discernible traits resolved from planktonic organisms in situ." ICES Journal of Marine Science 76, no. 7 (2019): 1959–72. http://dx.doi.org/10.1093/icesjms/fsz184.

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Abstract Trait-based simplifications of plankton community structure require accurate assessment of trait values as expressed in situ. Yet planktonic organisms live suspended in a fluid medium and often bear elongate appendages, delicate feeding structures, and mucous houses that are badly damaged upon capture or removal from the fluid environment. Fixatives further distort organisms. In situ imaging of zooplankton from a fully autonomous Zooglider reveals a suite of trait characteristics that often differ markedly from those inferred from conventionally sampled plankton. In situ images show fragile feeding appendages in natural hunting postures, including reticulate networks of rhizopods, feeding tentacles of cnidarians, and tentilla of ctenophores; defensive spines and setae of copepods; intact mucous houses of appendicularians; and other structures that are not discernible in conventionally collected zooplankton. Postures characteristic of dormant copepods can be identified and the presence of egg sacs detected. Intact, elongate diatom chains that are much longer than measured in sampled specimens are resolvable in situ. The ability to image marine snow, as well as small-scale fluid deformations, reveals micro-habitat structure that may alter organismal behaviour. Trait-based representations of planktonic organisms in biogeochemical cycles need to consider naturally occurring traits expressed by freely suspended planktonic organisms in situ.
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9

Mcnair, Heather, Courtney Nicole Hammond, and Susanne Menden-Deuer. "Phytoplankton carbon and nitrogen biomass estimates are robust to volume measurement method and growth environment." Journal of Plankton Research 43, no. 2 (2021): 103–12. http://dx.doi.org/10.1093/plankt/fbab014.

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Abstract Phytoplankton biomass is routinely estimated using relationships between cell volume and carbon (C) and nitrogen (N) content that have been defined using diverse plankton that span orders of magnitude in size. Notably, volume has traditionally been estimated with geometric approximations of cell shape using cell dimensions from planar two-dimensional (2D) images, which requires assumptions about the third, depth dimension. Given advances in image processing, we examined how cell volumes determined from three-dimensional (3D), confocal images affected established relationships between phytoplankton cell volume and C and N content. Additionally, we determined that growth conditions could result in 30–40% variation in cellular N and C. 3D phytoplankton cell volume measurements were on average 15% greater than the geometric approximations from 2D images. Volume method variation was minimal compared to both intraspecific variation in volumes (~30%) and the 50-fold variation in elemental density among species. Consequently, C:vol and N:vol relationships were unaltered by volume measurement method and growth environment. Recent advances in instrumentation, including those for at sea and autonomous applications can be used to estimate plankton biomass directly. Going forward, we recommend instrumentation that permits species identification alongside size and shape characteristics for plankton biomass estimates.
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10

Luo, T., K. Kramer, D. B. Goldgof, et al. "Errata to “Recognizing Plankton Images From the Shadow Image Particle Profiling Evaluation Recorder”." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (2004): 2423. http://dx.doi.org/10.1109/tsmcb.2004.837353.

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