Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Abstract

Plant diseases often reduce crop yield and product quality; therefore, plant disease diagnosis plays a vital role in farmers’ management decisions. Visual crop inspections by humans are time-consuming and challenging tasks and, practically, can only be performed in small areas at a given time, especially since many diseases have similar symptoms. An intelligent machine vision monitoring system for automatic inspection can be a great help for farmers in this regard. Although many algorithms have been introduced for plant disease diagnosis in recent years, a simple method relying on minimal information from the images is of interest for field conditions. In this study, a novel image processing algorithm and multi-class support vector machine (SVM) were used to diagnose and classify grape leaf diseases, i.e., black measles, black rot, and leaf blight. The area of disease symptoms was separated from the healthy parts of the leaf utilizing K-means clustering automatically, and then the features were extracted in three color models, namely RGB, HSV, and l*a*b. As an efficient classification method, SVM was used in this study, where principal component analysis (PCA) was performed for feature dimension reduction. Finally, the most important features were selected by the relief feature selection. Gray-level co-occurrence matrix (GLCM) features resulted in an accuracy of 98.71%, while feature dimension reduction using PCA resulted in an accuracy of 98.97%. The proposed method was compared with two deep learning methods, i.e., CNN and GoogleNet, which achieved classification accuracies of 86.82% and 94.05%, respectively, while the processing time for the proposed method was significantly shorter than those of these models.