TEXTURE FEATURES EXTRACTION TECHNOLOGY USING GREY LEVEL CO-OCCURRENCE MATRIX FOR THE KNN CLASSIFICATION OF CITRUS DISEASE

Abstract

The citrus disease is a problem affecting the decrease of agricultural commodity yields. One way to determine disease in citrus is through the leaves. Leaves, as a place for photosynthesis, with disease will cause stunted plant growth. Therefore, the fruit can experience a quality decrease. This study aims to classify citrus diseases based on leaf images by applying extraction technology of GLCM (Gray Level Co-occurrence Matrix) using KNN (K-Nearest Neighbor). Citrus disease classification has four main stages, namely preprocessing, segmentation, feature extraction, and classification. The preprocessing stage converts the LAB color space. Segmentation stage uses Otsu Thresholding. Texture features extraction uses GLCM. Classification uses KNN. KNN classification uses several distances, namely Chi-Square, City Block (Manhattan), Correlation, Cosine, Euclidean, and Hassanat. Comparisons are made based on the normalization of the dataset and the KNN distance used. The dataset without normalization gets the best results with Hassanat distance KNN (k = 29) with an accuracy of 91.86% and the dataset with normalization gets the best results at Euclidean distance (k = 7) with an accuracy of 98.84%. This research was expected to find out the accuracy of the method mentioned above in the classification of citrus diseases.