Imagen de Portada

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

Guardado en:
Publicado en:Journal of Marine Science and Engineering vol. 12, no. 1 (2024), p. 161
Autor principal: Capetillo-Contreras, Omar
Otros Autores: Pérez-Reynoso, Francisco David, Zamora-Antuñano, Marco Antonio, Álvarez-Alvarado, José Manuel, Rodríguez-Reséndiz, Juvenal
Publicado:
MDPI AG
Materias:
Water quality control
Food security
Sensors
World population
Image processing
Computer vision
Environmental impact
Quality control
Water monitoring
Proteins
Polyculture (aquaculture)
Water quality
Fishing
Food industry
Environmental management
Classification
Artificial intelligence
Engineering
Ponds
Aquaculture techniques
Aquaculture
Overexploitation
Salinity
Fish
Methods
Trends
Fisheries
Foods
Publications
Taxonomy
Economic
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:The world population is expected to grow to around 9 billion by 2050. The growing need for foods with high protein levels makes aquaculture one of the fastest-growing food industries in the world. Some challenges of fishing production are related to obsolete aquaculture techniques, overexploitation of marine species, and lack of water quality control. This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing. Through the systematic PRISMA process, 753 investigations published from 2012 to 2023 were analyzed based on a search in Scopus and Web of Science. It revealed a significant 70.5% increase in the number of articles published compared to the previous year, indicating a growing interest in this field. The results indicate that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision. Also, it is reported that K means technologies offer an efficiency from 95% to 98%. These methods allow decisions based on data patterns and aquaculture insights. Improving aquaculture methodologies will allow adequate management of economic and environmental resources to promote fishing and satisfy nutritional needs.
ISSN:2077-1312
DOI:10.3390/jmse12010161
Fuente:Engineering Database