Bean vision: a microcontroller-based intelligent hulling and sorting system for robusta coffee bean using yolo v8/ Laika Joy S. San Diego, Janna Victoria M. Cerera, Danilyn B. Magluyoan, Gio Sebastian O. Pacia, Kathleen Nicole S. Pascual, and John Kenneth F. Villaflores.--

Bachelor's thesis

College Of Engineering.--
Bachelor of science in electronics engineering: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

The Philippine coffee industry faces significant challenges in processing Robusta
coffee beans due to inefficiencies and quality inconsistencies caused by traditional manual
methods. This study proposes a hybrid system that integrates mechanical and electronic
technologies to automate the processing of Robusta coffee. The system includes a vibratory
mesh sorting mechanism that separates the beans into medium and large sizes, improving
consistency and uniformity. The mechanical hulling machine efficiently removes husks,
while YOLO V8 computer vision technology is incorporated for quality sorting. The
trained YOLO V8 model, based on a dataset of 11,400 samples, achieves an impressive
97% accuracy in detecting the correct bean quality classification. Although the speed of
the YOLO V8 model may not yet surpass manual sorting, its high accuracy offers
significant potential for enhancing quality control and reducing labor dependence in the
future. The system was evaluated using the ASEAN Standard for Coffee Beans (ASEAN
Stan 31: 2013) to ensure compliance with industry quality standards through the
verification of the Philippine Professional Coffee Cuppers to the beans used for data
collection and trained in the computer vision YOLO v8 algorithm model. The system
increased the sustainability and global competitiveness of the Philippine coffee sector by
modernizing traditional post-harvest processing methods.