Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico

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Ana Cristina Umaquinga Criollo

Resumen

En esta investigación, se presenta un análisis bibliométrico de 106 artículos de revistas y estado del arte indexados en Scopus, junto con un análisis sistemático de 83 artículos seleccionados. Se identifican áreas de estudio que incluye la predicción de rendimiento y crecimiento de cultivos, la detección de enfermedades en plantas, análisis de agua y suelo, relacionados con diferentes tipos de cultivo como: cereales (arroz, cebada, maíz, trigo, soya); frutas (manzana, pepino); legumbres (alfalfa, frejol, cacahuate); tubérculos, entre otros. Se examinan variables climáticas, suelo, agua, condiciones topográficas, edafológicas y técnicas de minería de datos como, Redes Neuronales, Deep Learning, segmentación, reglas de asociación y clasificación, entre otras, para optimizar el uso de recursos y tomar decisiones agrícolas basadas en datos. Además, se destacan los desafíos y oportunidades en esta área de investigación, así como las perspectivas futuras para el desarrollo de soluciones de minería de datos avanzadas en el contexto agrícola. Este análisis contribuye a una mejor comprensión de cómo la minería de datos está transformando el sector agrícola, comunidad académica y científica, con el fin de impulsar la eficiencia, la sostenibilidad y la toma de decisiones informadas en la producción de alimentos.

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Umaquinga Criollo, A. C. (2024). Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico. INNOVATION &Amp; DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES, 6(1), 25. https://doi.org/10.53358/ideas.v6i1.944
Sección
Information and Electronic Engineering

Citas

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