Segmentation Analysis in Powdery Mildew Infested (Sphaerotheca Fuliginea) Cucumber Leaves

Abstract

In this document, we propose the recognition of powdery mildew in cucumber leaves based on image processing. Two cucumber cycles were established and infested with powdery mildew. As the disease developed, photos were taken to perform the analysis. Two hundred photographs were manually preprocessed eliminating the background and leaving only leaves infested with the disease. The images were segmented using three threshold binarization techniques: gray scale binarization, RGB binarization and K-means algorithm with initially located centroids. The results were compared between the different methods. The gray scale binarization as well as the RGB binarization allowed locating the disease based on a percentage of the lighter shades, although the latter method analyzes each one of the different color layers. The K-means algorithm identified groups with similar characteristics around provided centers. A false positive detection test was also performed with 25 previously processed photographs. The experimental results show that the proposed gray scale binarization method better results for the recognition of the disease than the other methods.