Rice Leaf Disease Image Classifications Using KNN Based On GLCM Feature Extraction

Abstract

The disease that often occurs in rice leaves which causes crop failure. At present the attack of pests or leaf diseases in countries where the majority of rice as the main food of the community, especially in Indonesia is increasing. Early detection mechanism to reduce the risk of crop failure is very necessary. Data mining classification methods for the past few years have been very popular to be used in detecting the classification of rice leaf disease. Our paper uses 120 images of rice leaf disease from the UCI repository. The purpose of this study was to determine how to classify images of rice leaf disease consisting of three diseases namely Bacterial leaf blight, Brown spot, and Leaf smut. The study proposes the GLCM method as feature extraction for text analysis, with five feature values consisting of contrast, energy, entropy, homogeneity, and correlation. KNN (K-Nearest Neighbor) algorithm is used for the classification of rice leaf disease, by finding the maximum k value from the experiment k value 1 to 20. The results of our experiments show that the value of k = 11 has the highest accuracy value compared to other k values of 65.83% and kappa 0.485.