<![CDATA[Sugarcane is one of Indonesia's key plantation commodities with a critical role in fulfilling national sugar demand and supporting bioethanol production. However, sugarcane productivity remains low due to leaf diseases that reduce crop quality and yields, while slow or inaccurate identification accelerates their spread. This study proposes and develops a mobile-based sugarcane leaf disease detection system using transfer learning with the MobileNetV3Large architecture to classify 11 disease classes. Two dataset scenarios were applied: Scenario 1 using the SLD Dataset with 6,748 images and Scenario 2 combining the SLD and Sugarcane Smut datasets totaling 14,804 images. Each scenario was trained under three optimizer configurations: Adam, RMSprop, and SGD, to identify the best-performing combination. Results show that Adam achieved the highest validation accuracy in both scenarios, reaching 94.24% in Scenario 1 and 97.43% in Scenario 2, with corresponding test accuracies of 94.91% and 97.31% respectively. The final model was deployed as a Flutter-based mobile application capable of performing real-time disease detection through image upload or camera capture, providing an accessible tool for farmers to identify sugarcane leaf diseases efficiently.]]>
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