Session S20 - Applied Math and Computational Methods and Analysis across the Americas

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Diagnosis of diseases in plants using Gaussian Mixture Models and Probabilistic Saliency.

Lili Guadarrama Bustos

CIMAT, Mexico   -   This email address is being protected from spambots. You need JavaScript enabled to view it.

A simple and robust approach is presented for plant disease diagnosis by means of a neural network through images of plants even in uncontrolled environments, identifying and quantifying the colors associated with the diseases for the purpose of estimating the portion of the plant that has presence of diseased tissue. In order to improve the performance of the neural network, Gaussian Mixture Models and Probabilistic Saliency are used to accurately segment the plant from the background of an image.

Joint work with Carlos Paredes (Centro de Investigación en Óptica) and Omar Mercado (CIMAT).

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