Identification of Lasiodiplodia Theobromae in avocado trees through image processing and machine learning

Heber I. Mejía-Cabrera, J. Nicolás Flores, Jack Sigueñas, Victor Tuesta-Monteza, Manuel G. Forero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


The avocado is a fruit that grows in tropical and subtropical areas, very popular in the markets due to its great nutritional qualities and medicinal properties. The avocado is a plant of great commercial interest for Peru and Colombia, countries that export this fruit. This tree is affected by a wide variety of diseases reducing its production, even causing the death of the plant. The most frequent disease of the avocado tree in the production zone of Peru is caused by the fungus Lasiodiplodia Theobromae, which is characterized in its initial stage by producing a chancre around the stems and branches of the tree. Detection is commonly made by manual inspection of the plants by an expert, which makes it difficult to detect the fungus in extensive plantations. Therefore, in this work we present a semi-automatic method for the detection of this disease based on image processing and machine learning techniques. For this purpose, an acquisition protocol was defined. The identification of the disease was performed by taking as input pre-processed images of the tree branches. A learning technique was evaluated, based on a shallow CNN, obtaining 93% accuracy.

Idioma originalInglés
Título de la publicación alojadaApplications of Digital Image Processing XLIII
EditoresAndrew G. Tescher, Touradj Ebrahimi
ISBN (versión digital)9781510638266
EstadoPublicada - 2020
Publicado de forma externa
EventoApplications of Digital Image Processing XLIII 2020 - Virtual, Online, Estados Unidos
Duración: 24 ago. 20204 set. 2020

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X


ConferenciaApplications of Digital Image Processing XLIII 2020
País/TerritorioEstados Unidos
CiudadVirtual, Online

Nota bibliográfica

Publisher Copyright:
© 2020 SPIE


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