Implementation of Gray Level Coocurence Matrix on the Leaves of Rice Crops

Abstract

Rice is one of the cultivation plants that are very important for human survival. The success of rice harvesting affects the level of farmers' income. However, farmers often suffer losses as a result of illness in rice. Rice plants infected with the disease will show symptoms in the form of patches that have certain patterns and colors on some parts of the body of rice plants, such as stems, leaves, and roots. Disease symptoms that emerge on the leaves are most easily identified because the leaves have a wider cross-section than other body parts of rice. Therefore, in this study, the leaf was used as an initial step parameter for disease detection in rice. This research aimed to identify diseases that exist in rice plants using the method of Gray Level Co-occurrence Matrix (GLCM). The GLCM method is a feature extraction method. The disease detection process on the leaves of the rice plants was done by retrieving the original image for the initial step; then, the original image was segmented before converted to greyscale imagery. After that, feature extraction was carried out using the GLCM features: Entropy, Eccentricity, Contrast, Energy, Correlation, Homogeneity. The results showed 90% accuracy results using GLCM extraction. The recognition of the emerging diseases on rice leaves can help to identify the type of disease infecting the rice plants.