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논문 기본 정보

자료유형
학술저널
저자정보
이예찬 (부산대학교) 노이현 (부산대학교) 노윤서 (부산대학교) 김주송 (부산대학교) 류광열 (부산대학교) 김병학 (한국생산기술연구원)
저널정보
제어로봇시스템학회 제어로봇시스템학회 논문지 제어로봇시스템학회 논문지 제30권 제11호
발행연도
2024.11
수록면
1,245 - 1,253 (9page)
DOI
10.5302/J.ICROS.2024.24.0204

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초록· 키워드

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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.

목차

Abstract
I. 서론
II. 본론
III. 실험 환경 및 결과
IV. 결론
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