인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and processed in various ways, and the functionality of processing equipment is advanced to optimize its quality. In particular, coffee roasting equipment has evolved to incorporate complex structures and advanced electronic, mechanical, and heat/air control technologies for processing harvested green coffee beans. One of the most critical quality factors in the roasting process is the degree of roast, which is measured by the bean color (Agtron) value. The conventional method for Agtron measurement involves grinding the beans and using vision sensors. However, the conventional method shows difficult and time-consuming. The method for measuring the color of whole beans without grinding is simpler, but it has limitations in accuracy due to variations in shading and background images. This study proposes a method to accurately measure the Agtron color of whole coffee beans without grinding. The proposed method employs a preprocessing technique that separates the measurement area from the background area through color histogram analysis. To enhance measurement accuracy, we suggest using a Lasso regression model, which mitigates overfitting problems. Experimental results demonstrate that the proposed method achieves an improved accuracy compared with traditional regression methods and neural network-based approaches.
본문·목차
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