Aprendizaje automático para la detección de plásticos marinos: análisis bibliométrico de la producción científica
Machine learning for marine plastic detection: a bibliometric analysis of scientific productionBarra lateral del artículo
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Contexto: La contaminación por plásticos marinos es un problema ambiental creciente que afecta ecosistemas costeros y oceánicos. El aprendizaje automático ha emergido como una herramienta clave para mejorar la detección, clasificación y monitoreo de estos residuos mediante el uso de imágenes satelitales, aéreas y submarinas. Objetivo: Analizar la evolución, estructura científica y tendencias de la investigación sobre aprendizaje automático aplicado a la detección de plásticos marinos. Metodología: Se realizó un estudio bibliométrico de artículos indexados en Scopus publicados entre 2000 y 2025. Los registros fueron seleccionados mediante un proceso sistemático basado en PRISMA y analizados con Bibliometrix en RStudio. Se evaluaron tendencias de publicación, revistas, autores, instituciones, citas, distribución geográfica, redes de colaboración y estructura temática. Resultados: Se evidenció un crecimiento sostenido de publicaciones desde 2020, con predominio de enfoques basados en aprendizaje profundo, visión por computadora, sensores remotos y detección automatizada. La producción científica se concentra en revistas de ciencias ambientales, contaminación marina y teledetección, con mayor aporte de Asia y Europa. Las redes de colaboración internacional muestran una estructura fragmentada. Conclusiones: La investigación en aprendizaje automático aplicado a plásticos marinos ha evolucionado hacia enfoques altamente especializados. Sin embargo, persisten brechas en la estandarización de datos, la comparabilidad de modelos y la cooperación interregional, lo que limita la consolidación de avances globales en el área.
Context: Marine plastic pollution is an escalating environmental problem affecting coastal and oceanic ecosystems. Machine Learning has emerged as a pivotal tool for improving the detection, classification, and monitoring of these residues through the use of satellite, aerial, and underwater imagery. Objective: This study aims to analyze the evolution, scientific structure, and research trends of Machine Learning applied to marine plastic detection. Methodology: A bibliometric study of Scopus-indexed articles published between 2000 and 2025 was conducted. Records were selected through a systematic process based on PRISMA guidelines and analyzed with Bibliometrix in RStudio. Publication trends, journals, authors, institutions, citations, geographic distribution, collaboration networks, and thematic structure were evaluated. Results: A sustained growth in publications has been evidenced since 2020, with a predominance of approaches based on Deep Learning, computer vision, remote sensing, and automated detection. Scientific production is concentrated in journals of environmental sciences, marine pollution, and remote sensing, with the largest contributions from Asia and Europe. International collaboration networks show a fragmented structure. Conclusions: Research in Machine Learning applied to marine plastics has evolved toward highly specialized approaches. However, gaps remain in data standardization, model comparability, and interregional cooperation, limiting the consolidation of global advances in the area.
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