Authors - Maya Fitria, Muhammad Hafiz Rinaldi, Khairun Saddami, Isack Farady, Kahlil Muchtar, Sayed Muchallil Abstract - As the most consumed commodity worldwide, banana requires careful and proper growth management to maintain its production, including maintaining its leaf health. Commonly, farmers identify the disease in banana leaves by inspecting its appearance. However, this conventional method is considered subjective to one person to another, and this could lead to delayed treatment, and may impact the fruit development and production. To address this issue, this re-search proposed B-Leaf Scanner, a mobile-based application integrating a deep learning approach for banana leaf disease detection. The application integrated the YOLOv5-based model to detect and classify the disease in banana leaf which is conducted by capturing image from a camera or by inputting from the device gallery. The proposed application was designed aligned with the findings from field observations and interviews with local farmers to ensure usability and related to real-world settings. The findings show that the detection model yielded an mAP of 80.1%, following with 86.8% and 72.4% of precision and recall value, respectively. These results indicate the reliability of the model in performing the detection process. Moreover, the usability testing of the application was con-ducted to ten local farmers through task-based testing, and System Usability Scale (SUS). Based on usability results, the B-Leaf Scanner application achieved excellent usability with a SUS score of 88%, indicating the application can effectively support local banana farmers in identifying leaf diseases.
