<![CDATA[The detection of diseases in mango leaves is a significant challenge in Indonesia’s agricultural sector, as it directly affects both the quality and quantity of crop yields. This study aims to develop an image-based classification system for mango leaf diseases using Convolutional Neural Network (CNN) architectures, namely VGG16 and Xception. The dataset used in this research consists of two different datasets. The first dataset includes two classes, healthy and diseased leaves, while the second dataset comprises three classes: Jelangga Fungus, Chlorosis, and Healthy. Data augmentation techniques and the Adam optimizer were applied to enhance model performance. Model evaluation was conducted using a confusion matrix along with precision, recall, and F1-score metrics. The experimental results indicate that VGG16 consistently achieves the best performance, with an accuracy of up to 100% in the two-class classification scenario and 99% in the three-class classification scenario. These findings demonstrate that CNN architectures, particularly VGG16, are effective and reliable for classifying mango leaf diseases based on digital images.]]>
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