Dataset Information

ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.

ABSTRACT: Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.

SUBMITTER: Arellano-Verdejo J

PROVIDER: S-EPMC6500371 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.

Arellano-Verdejo Javier J Lazcano-Hernandez Hugo E HE Cabanillas-Terán Nancy N

PeerJ 20190501

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae a ...[more]

PMID: 31106059

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Project description:BACKGROUND AND AIMS:On the basis of morphological evidence, the species involved in South American Pacific coast harmful algal blooms (HABs) has been traditionally recognized as Alexandrium catenella (Dinophyceae). However, these observations have not been confirmed using evidence based on genomic sequence variability. Our principal objective was to accurately determine the species of Alexandrium involved in local HABs in order to implement a real-time polymerase chain reaction (PCR) assay for its rapid and easy detection on filter-feeding shellfish, such as mussels. METHODOLOGY:For species-specific determination, the intergenic spacer 1 (ITS1), 5.8S subunit, ITS2 and the hypervariable genomic regions D1-D5 of the large ribosomal subunit of local strains were sequenced and compared with two data sets of other Alexandrium sequences. Species-specific primers were used to amplify signature sequences within the genomic DNA of the studied species by conventional and real-time PCR. PRINCIPAL RESULTS:Phylogenetic analysis determined that the Chilean strain falls into Group I of the tamarensis complex. Our results support the allocation of the Chilean Alexandrium species as a toxic Alexandrium tamarense rather than A. catenella, as currently defined. Once local species were determined to belong to Group I of the tamarensis complex, a highly sensitive and accurate real-time PCR procedure was developed to detect dinoflagellate presence in Mytilus spp. (Bivalvia) samples after being fed (challenged) in vitro with the Chilean Alexandrium strain. The results show that real-time PCR is useful to detect Alexandrium intake in filter-feeding molluscs. CONCLUSIONS:It has been shown that the classification of local Alexandrium using morphological evidence is not very accurate. Molecular methods enabled the HAB dinoflagellate species of the Chilean coast to be assigned as A. tamarense rather than A. catenella. Real-time PCR analysis based on A. tamarense primers allowed the detection of dinoflagellate DNA in Mytilus spp. samples exposed to this alga. Through the specific assignment of dinoflagellate species involved in HABs, more reliable preventive policies can be implemented.

Dataset Information

ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.

Publications

ERISNet: deep neural network for <i>Sargassum</i> detection along the coastline of the Mexican Caribbean.

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