<![CDATA[Diseases affecting shallot plants are a primary cause of reduced crop quality and yield. Manual disease identification relies on visual observation, making it prone to error and time-consuming. This study aims to develop a classification model for shallot leaf diseases by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction with MobileNetV2, classified using a Convolutional Neural Network (CNN). The dataset comprises 1,188 shallot leaf images categorized into five classes: downy mildew, healthy, leaf blight, *moler* (basal rot), and purple blotch. The research process involved dataset collection; pre-processing (image resizing to 224×224 pixels, grayscale conversion, normalization, and data augmentation); texture feature extraction using GLCM; and deep feature extraction using MobileNetV2. These features were then combined and used as input for the CNN classification model. Model evaluation was conducted using a confusion matrix, assessing accuracy, precision, recall, and F1-score. The results demonstrate that the proposed model achieved 93% accuracy, with balanced precision, recall, and F1-score values across most classes. The contribution of this research is the integration of gray level co-occurrence matrix (GLCM) texture feature extraction with mobilenetv2 visual features within a convolutional neural network (CNN) model to improve the classification performance of shallot leaf diseases.]]>
Copyrights © 2026
