Kim Youngjin, Lee Jooho, Kim Sangoh
Department of Plant and Food Engineering, Sangmyung University, Sangmyeongdae-gil 31, Dongnam-gu, Cheonan, Chungcheongnam-do 31066 Republic of Korea.
Food Sci Biotechnol. 2024 Jan 28;33(11):2543-2550. doi: 10.1007/s10068-023-01507-7. eCollection 2024 Aug.
In the modern food processing industry, which is more complex than in the past, it is important to utilize real-time computer vision for active food processing technology using artificial intelligence. An integrated solution of computer vision and Deep Learning (DL) technology provides quality control and optimization of food processing in complex environments with obstacles. In this study, Coffee Bean Classification Model (CBCM) made by Machine Learning (ML) showed excellent performance, accurately distinguishing coffee beans through the avoidance of obstacles and empty spaces inside a rotating roasting machine. CBCM achieved a maximum validation accuracy of 98.44% and a minimum validation loss of 5.40% after the fifth epoch. Using a test dataset of 137 samples, CBCM achieved an accuracy of 99.27% and a loss of 2.82%. The developed solution using the CBCM was able to quantify the color change of the coffee beans during roasting.
在比过去更复杂的现代食品加工业中，利用实时计算机视觉实现基于人工智能的主动食品加工技术非常重要。计算机视觉和深度学习（DL）技术的集成解决方案可在存在障碍物的复杂环境中实现食品加工的质量控制和优化。在本研究中，通过机器学习（ML）制作的咖啡豆分类模型（CBCM）表现出色，能够在旋转烘焙机内避开障碍物和空隙，准确区分咖啡豆。CBCM在第五个训练周期后实现了98.44%的最大验证准确率和5.40%的最小验证损失。使用137个样本的测试数据集，CBCM实现了99.27%的准确率和2.82%的损失率。使用CBCM开发的解决方案能够量化烘焙过程中咖啡豆的颜色变化。
