Plant Disease Diagnosis in the Visible Spectrum

Abstract

A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios.