Detection of Corn Leaf Blight Disease Based on GLCM and HSV Feature Extraction Using Support Vector Machine - Chen, Sami'un, Defitroh; Dahur , Arnoldus Janssen; Manalu, Mamed; Arif, M Aidil; Jaya, Dery Yuswanto (Jurnal Multimedia dan Teknologi Informasi (Jatilima), 2026)
Corn leaf blight is a major disease that reduces crop productivity, making early detection essential. This study proposes an image-based detection method using Gray Level Co-occurrence Matrix (GLCM) and HSV feature extraction with Support Vector Machine (SVM) classification. The dataset, obtained from Kaggle, consists of 2308 corn leaf images categorized into healthy and blight classes. The method includes preprocessing, segmentation, feature extraction, and classification. Preprocessing involves resizing, grayscale conversion, noise reduction, and normalization. Segmentation is performed using Otsu thresholding and K-Means clustering to isolate leaf regions and highlight disease areas. Feature extraction combines four GLCM texture features and three HSV color features to represent each image. The SVM model, evaluated using an 80:20 data split, achieved an accuracy of 94.8% with balanced precision, recall, and F1-score values of approximately 0.95. These results indicate that the proposed method is effective for detecting corn leaf blight and has potential for practical agricultural applications