Mosaic and rust are sugarcane diseases that happen in Indonesia and has considerable economic impact. Information technology for sugarcane disease detection is useful in supporting optimal sugarcane production. Most of current researches are about plant disease identification in general. There is no specific research about identification of sugarcane disease. This research proposes a sugarcane disease identification from sugarcane leaf image with gray level co-occurrence matrix (GLCM) and color moments. This research begins with collecting data from field survey. After sugarcane leaf images are captured through a field survey, they are preprocessed in order to be used in the features extraction step. Extracted features from these images are texture and color. Texture feature extraction is conducted by GLCM while color feature extraction is conducted by color moments. Classification method which is used in this research is support vector machine (SVM). Test conducted to find distinctive feature that has a significant impact in classification, there are 4 scenario to test the effects in deletion of shape feature, selection of texture and color feature, and also combination of texture and color feature. Texture feature with GLCM correlation, energy, homogeneity and variance combined with color moments 1, 2 and 3 for color feature extraction in 4th scenario is an appropriate feature for identification of sugarcane leaf disease with 97% classification accuracy.
Dewi, R. K., & Ginardi, R. V. H. (2017). Identifikasi Penyakit pada Daun Tebu dengan Gray Level Co-Occurrence Matrix dan Color Moments. Jurnal Teknologi Informasi Dan Ilmu Komputer, 1(2), 70. https://doi.org/10.25126/jtiik.201412114