Registrado por

LAZCANO HERNÁNDEZ HUGO ENRIQUE

Tipo de artículo

Publicado con arbitraje

Título de artículo

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

Volúmen

PeerJ 7:e6842

Número de registro

https://doi.org/10.7717/peerj.6842

Campo

CIENCIAS DE LA TIERRA Y EL COSMOS

Disciplina

OTRAS ESPECIALIDADES EN MATERIA DE CIENCIAS DE LA TIERRA DEL COSMOS Y DEL MEDIO AMBIENTE

Subdisciplina

Resumen

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.