<![CDATA[In agriculture, technology can provide benefits to farmers. However, at present there are still very few farmers who use technology, especially computerization in their agricultural processes, such as the identification of diseases in rice plants, there are still many rice farmers who cannot recognize and distinguish the types of diseases in rice plants. Research on the identification of bacterial leaf blight and brown spots on rice plants has carried out before, but the accuracy rate is only 70%. This research developed a system to identify bacterial leaf blight and brown spot in rice plants through leaf images with an image processing approach. Image of affected rice leaves is segmented first using K-Means Clustering, then the texture features are extracted using the Gray Level Co-Occurrence Matrix (GLCM) with features extracted in the form of energy, contrast, correlation, homogeneity and shape pattern characteristics using metric and eccentricity features, then identified using Euclidean Distance. The training data used 40 images for each disease and 12 images for each disease. The test results show that the system has a better level of accuracy than previous studies that reached 100% with a Mean Squared Error (MSE) value of 0.007282214.]]>
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