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Video Experimental Relacionado

Updated: Sep 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Mapeo de malezas utilizando imágenes de UAV y técnicas de IA: tendencias y desafíos actuales

Maurício Cagliari Tosin1, Aldo Merotto Júnior1, Estéfani Sulzbach1

  • 1Crop Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.

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PubMed
Resumen

Las técnicas de aprendizaje profundo son prometedoras para la identificación de malezas en tiempo real en la agricultura utilizando imágenes de drones. Esta revisión analiza los métodos de aprendizaje automático para el mapeo de malezas, destacando los desafíos y las direcciones futuras para la agricultura de precisión.

Palabras clave:
Inteligencia artificialvisión por computadoraProcesamiento de imágenesAprendizaje automáticoredes neuronalesagricultura de precisiónManejo de malezas específicas del sitio

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Área de la Ciencia:

  • Ingeniería agrícola
  • Ciencias de la computación
  • Detección remota

Sus antecedentes:

  • Los vehículos aéreos no tripulados (UAV) logran una alta precisión en el reconocimiento de malezas (> 90%).
  • La identificación de malezas en tiempo real en los sistemas integrados sigue siendo un desafío.
  • El mapeo de malezas basado en UAV es crucial para la agricultura de precisión.

Objetivo del estudio:

  • Revisar y analizar las aplicaciones de aprendizaje automático y aprendizaje profundo (DL) para el reconocimiento de malezas utilizando imágenes de UAV.
  • Resaltar las metodologías, los desafíos y las ventajas/limitaciones de la investigación actual.
  • Para orientar futuras investigaciones en el mapeo de malezas en tiempo real y la aplicación de herbicidas específicos del sitio.

Principales métodos:

  • Revisión sistemática de la literatura de la investigación académica sobre el reconocimiento de malezas basado en UAV.
  • Organización y comparación de estudios basados en la metodología y las cuestiones abordadas.
  • Análisis de los enfoques clásicos y DL para la extracción y clasificación de características.

Principales resultados:

  • Los métodos clásicos se centran en las características espectrales, textuales y geométricas, con una tendencia hacia los espectros no visibles.
  • Los métodos DL sobresalen en la extracción automática de características de múltiples escalas directamente de las imágenes.
  • DL muestra una promesa significativa para distinguir las especies y tipos de maleza.

Conclusiones:

  • Las imágenes de UAV combinadas con DL ofrecen un enfoque poderoso para el mapeo de malezas.
  • Se necesita más investigación para superar los desafíos en los sistemas integrados en tiempo real.
  • Esta revisión proporciona información para el desarrollo de sistemas inteligentes para el manejo de malezas específicas del sitio.