Análisis de redes neuronales artificiales para el reconocimiento automático de plantas

• Jorge Saa-Saltos ^[1] ; Llanos Mora-López ^[2] ; José del Campo-Ávila ^[2]
  1. [1] Universidad Técnica Estatal de Quevedo

     Universidad Técnica Estatal de Quevedo

     Quevedo, Ecuador

     
  2. [2] Universidad de Málaga

     Universidad de Málaga

     Málaga, España

     
• Localización: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, ISSN-e 1646-9895, Nº. Extra 52, 2022, págs. 52-67
• Idioma: español
• Títulos paralelos:
  • Development of Programming Logic in Systems Development students
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    La amplia variedad y uso de redes neuronales artificiales (ANN) resulta complejo en el campo de procesamiento de imágenes. El objetivo es determinar la ANN más empleada y el entorno de trabajo para la identificación automática de imágenes de hojas vegetales. La investigación se desarrolló en una revisión de 40 artículos. La metodología consistió en la identificación del órgano de la planta más idóneo para su reconocimiento por visión artificial, seguido del análisis de distintos modelos de redes, su arquitectura y procesamiento de imágenes. Los resultados determinan que la red neuronal convolucional (CNN) es la más utilizada entre 9 tipo de ANN, y en el procesamiento de imágenes se diferencian dos fases primordiales, pre-procesamiento y entrenamiento. En conclusión, la CNN tiene los mejores resultados por sus altos porcentajes de precisión y su implementación es cada vez mayor en el campo de la visión artificial.

  • English

    The wide variety and use of artificial neural networks (ANN) is complex in the field of image processing. The objective is to determine the most commonly used ANN and the working environment for automatic identification of plant leaf images. The research was developed in a review of 40 articles. The methodology consisted of the identification of the most suitable plant organ for recognition by artificial vision, followed by the analysis of different network models, their architecture and image processing. The results determine that the convolutional neural network (CNN) is the most used among 9 types of ANNs, and in the image processing two main phases are differentiated, pre-processing and training. In conclusion, the CNN has the best results due to its high accuracy percentages and its implementation is increasing in the field of artificial vision.

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