000			02888nam a22003377a 4500
003			OSt
005			20250718133639.0
008			250718b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bD26 2025
100			_aSan Diego, Laika Joy S. _eauthor
245			_aBean vision: _ba microcontroller-based intelligent hulling and sorting system for robusta coffee bean using yolo v8/ _cLaika Joy S. San Diego, Janna Victoria M. Cerera, Danilyn B. Magluyoan, Gio Sebastian O. Pacia, Kathleen Nicole S. Pascual, and John Kenneth F. Villaflores.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_axiv, 232pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege Of Engineering.-- _bBachelor of science in electronics engineering: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThe 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.
650			_aHybrid automation system
650			_aMechanical hulling machine
650			_aYOLOv8 computer vision
700			_aCerera, Janna Victoria M. _eauthor
700			_aMagluyoan, Danilyn B. _eauthor
700			_aPacia, Gio Sebastian O. _eauthor
700			_aPascual, Kathleen Nicole S. _eauthor
700			_aVillaflores, John Kenneth F. _eauthor
942			_2lcc _cBTH COE _n0
999			_c30451 _d30451