Natural rubber is one of Indonesia's most important export commodities, making the country the second largest exporter globally with a 28.65% share of the world market. However, recent production has declined, partly due to leaf fall disease caused by the Pestalotiopsis sp. fungus. This disease leads to premature leaf drop, which forces rubber trees to redirect energy from latex production to leaf regeneration, potentially reducing yields by up to 30%. Traditional detection methods that rely on manual visual inspection of leaf morphology are impractical over large plantation areas. To address this, the present study proposes a remote sensing-based detection approach using aerial drone imagery and unsupervised machine learning. Two feature extraction methods: Convolutional Autoencoder (CAE) and Gray Level Co-occurrence Matrix (GLCM) were used prior to clustering with k-means. Despite a small dataset, the GLCM-based approach significantly outperforms the CAE-based method. These results demonstrate that GLCM combined with clustering can reliably distinguish between healthy and diseased plantation areas. The proposed method offers a cost-effective, scalable, and non-invasive alternative to ground surveys, and has strong potential for real world deployment in disease monitoring and early warning systems across large agricultural regions. © (2025), (Science and Information Organization). All rights reserved.