Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans
Abstract
Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
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Copyright (c) 2025 Eko Dwi Nugroho, Miranti Verdiana, Muhammad Habib Algifari, Aidil Afriansyah, Hafiz Budi Firmansyah, Alya Khairunnisa Rizkita, Richard Arya Winarta, David Gunawan
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