<![CDATA[Rice (Oryza sativa ) is a major food staple, which is prone to multiple diseases that will dramatically decrease the harvest yield. Disease identification is time consuming and is usually subject to subjective errors in a manual approach. The following research will seek to increase the level of precision of automatic rice plant disease detection, namely the Brown Spot, Hispa, and Leaf Blast classes. The suggested method combines both the Gray Level Co-occurrence Matrix (GLCM) to extract texture features and the Extreme Gradient Boosting (XGBoost) classification algorithm. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance within the dataset of 5,548 images. Preprocessing steps include resizing, grayscale conversion, and Min-Max normalization. Experimental results demonstrate that the model trained on SMOTE-balanced data with optimized XGBoost parameters achieved a superior accuracy of 98%, outperforming the imbalanced scenario (97%) and previous studies. This research confirms that the combination of GLCM, SMOTE, and XGBoost constitutes a robust and high-precision method for rice disease identification]]>
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