Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis
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| Publicado en: | Journal of Marine Science and Engineering vol. 12, no. 1 (2024), p. 161 |
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| Autor principal: | |
| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 2918777524 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse12010161 |2 doi | |
| 035 | |a 2918777524 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Capetillo-Contreras, Omar |u Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; <email>ccapetillo08@alumnos.uaq.mx</email> | |
| 245 | 1 | |a Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Water quality control | ||
| 653 | |a Food security | ||
| 653 | |a Sensors | ||
| 653 | |a World population | ||
| 653 | |a Image processing | ||
| 653 | |a Computer vision | ||
| 653 | |a Environmental impact | ||
| 653 | |a Quality control | ||
| 653 | |a Water monitoring | ||
| 653 | |a Proteins | ||
| 653 | |a Polyculture (aquaculture) | ||
| 653 | |a Water quality | ||
| 653 | |a Fishing | ||
| 653 | |a Food industry | ||
| 653 | |a Environmental management | ||
| 653 | |a Classification | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Engineering | ||
| 653 | |a Ponds | ||
| 653 | |a Aquaculture techniques | ||
| 653 | |a Aquaculture | ||
| 653 | |a Overexploitation | ||
| 653 | |a Salinity | ||
| 653 | |a Fish | ||
| 653 | |a Methods | ||
| 653 | |a Trends | ||
| 653 | |a Fisheries | ||
| 653 | |a Foods | ||
| 653 | |a Publications | ||
| 653 | |a Taxonomy | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Pérez-Reynoso, Francisco David |u Laboratorio Nacional de Investigación en Tecnologías Médicas (LANITEM), Centro de Ingeniería y Desarrollo Industrial (CIDESI), Querétaro 76125, Mexico; <email>investigador3.lanitem@cidesi.edu.mx</email> | |
| 700 | 1 | |a Zamora-Antuñano, Marco Antonio |u Centro de Investigación, Innovación y Desarrollo Tecnológico (CIIDETEC-UVM), Universidad del Valle de México, Querétaro 76230, Mexico; <email>marco.zamora@uvmnet.edu</email> | |
| 700 | 1 | |a Álvarez-Alvarado, José Manuel |u Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; <email>ccapetillo08@alumnos.uaq.mx</email> | |
| 700 | 1 | |a Rodríguez-Reséndiz, Juvenal |u Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; <email>ccapetillo08@alumnos.uaq.mx</email> | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 12, no. 1 (2024), p. 161 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2918777524/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2918777524/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2918777524/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |