Interpretabilidad en el campo de la detección de enfermedades en las plantas: Una revisión
Resumen
La detección temprana de enfermedades en las plantas mediante técnicas de inteligencia artificial, ha sido un avance tecnológico muy importante para la agricultura, ya que por medio del aprendizaje automático y algoritmos de optimización, se ha logrado incrementar el rendimiento de diversos cultivos en varios países alrededor del mundo. Distintos investigadores han enfocado sus esfuerzos en desarrollar modelos que permitan apoyar la tarea de detección de enfermedades en las plantas como solución a las técnicas tradicionales utilizadas por los agricultores. En esta revisión sistemática de literatura se presenta un análisis de los artículos más relevantes, en los que se usaron técnicas de procesamiento de imágenes y aprendizaje automático, para detectar enfermedades por medio de imágenes de las hojas de diferentes cultivos, y a su vez se lleva a cabo un análisis de interpretabilidad y precisión de estos métodos, teniendo en cuenta cada fase las fases de procesamiento de imágenes, segmentación, extracción de características y aprendizaje, de cada uno de los modelos. De esta manera se evidencia vacío en el campo de la interpretabilidad, ya que los autores se han enfocado principalmente en obtener buenos resultados en sus modelos, más allá de brindar al usuario una explicación clara de las características propias del modelo.
Palabras clave
Aprendizaje Automático, Clasificación, Detección temprana de enfermedades, Interpretabilidad, Procesamiento de imágenes
Biografía del autor/a
Daniel-David Leal-Lara
Roles: Conceptualización, Investigación, Contenido - Curación, Metodología, Escritura - Borrador original, Escritura – Revisión y edición.
Julio Barón-Velandia
Roles: Conceptualización, Metodología, Escritor - Borrador Original, Escritura – Revisión y edición.
Camilo-Enrique Rocha-Calderón
Roles: Validación, Escritura – Revisión & edición.
Citas
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