Inteligencia artificial en acuicultura: fundamentos, aplicaciones y perspectivas futuras

Wilfredo  Vásquez-Quispesivana; Marianela  Inga; Indira  Betalleluz-Pallardel

doi:10.17268/sci.agropecu.2022.008

Autores/as

Wilfredo Vásquez-Quispesivana Facultad de Pesquería, Universidad Nacional Agraria La Molina. Av. La Molina s/n, La Molina, Lima.
Marianela Inga Facultad de Industrias Alimentarias, Universidad Nacional Agraria La Molina. Av. La Molina s/n, La Molina, Lima.
Indira Betalleluz-Pallardel Facultad de Industrias Alimentarias, Universidad Nacional Agraria La Molina. Av. La Molina s/n, La Molina, Lima.

DOI:

https://doi.org/10.17268/sci.agropecu.2022.008

Palabras clave:

Acuicultura, inteligencia artificial, redes neuronales, aprendizaje automático, aprendizaje profundo, optimización

Resumen

Los avances en las tecnologías de manejo de datos se están adecuando a resolver dificultades e impactos que la acuicultura manifiesta, algunos aspectos que a través de los años no se han podido manejar plenamente, ahora son más factibles de resolver, como la optimización de las variables que intervienen en el crecimiento e incremento de biomasa, la predicción de parámetros de calidad de agua para manejar y tomar decisiones durante el cultivo, la evaluación del medio ambiente acuícola y el impacto que genera la acuicultura, el diagnóstico de enfermedades de los peces para determinar tratamientos más puntuales, el manejo, gestión y cierre de granjas acuícolas. El objetivo del presente artículo fue revisar dentro de los últimos 20 años las diversas técnicas, metodologías, modelos, algoritmos, softwares y dispositivos que se utilizan dentro de los sistemas de inteligencia artificial, aprendizaje automático y aprendizaje profundo, para resolver de una manera más sencilla, rápida y precisa las dificultades e impactos que la acuicultura evidencia. Además, se explican los fundamentos de la inteligencia artificial, aprendizaje automático y aprendizaje profundo, así también las recomendaciones de estudio futuro sobre áreas de interés en acuicultura, como la reducción de los costos de producción mediante la optimización de la alimentación en función de las buenas prácticas de acuicultura y parámetros de calidad de agua, la identificación del sexo en peces que no presentan dimorfismo sexual, la determinación de atributos de calidad como el grado de pigmentación en salmones y truchas.

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