Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

Julio Barón Velandia (Universidad Distrital “Francisco José de Cáldas")
Camilo Enrique Rocha Calderón (Universidad Distrital “Francisco José de Cáldas")
Daniel David Leal Lara (Universidad Distrital “Francisco José de Cáldas")

International Journal of Electrical and Computer Engineering
Vol 11, No 4: August 2021

This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.