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Title: Identification of Lasiodiplodia Theobromae in avocado trees through image processing and machine learning
Authors: Mejiá-Cabrera, Heber I.
Flores, J. Nicolás
Sigueñas, Jack
Tuesta-Monteza, Victor
Forero, Manuel G.
Keywords: Avocado
Lasiodiplodia Theobromae
Tree diseases
Artificial neural networks
Machine learning
Image processing
Acquisition protocol
Issue Date: 24-Aug-2020
Publisher: Proceedings of SPIE - The International Society for Optical Engineering
Citation: Heber I. Mejiá-Cabrera, J. Nicolás Flores, Jack Sigueñas, Victor Tuesta-Monteza, and Manuel G. Forero "Identification of Lasiodiplodia Theobromae in avocado trees through image processing and machine learning", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115102F (21 August 2020);
Abstract: 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.
ISSN: 0277-786X
Appears in Collections:Artículos

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