Abstract
Coffee bean quality screening is a time-consuming work, and its workload increases abruptly with the rapid development of coffee beverage consumer market. In this work, a CNN-based classifier is developed to categorizing the coffee beans into sour, black, broken, moldy, shell, insect damage and good beans. The screening test results show that the screening accuracy could reach more than 90% for all other beans except for shell beans (88%). Therefore, the proposed method is feasible and promising. Moreover, a cost-effective automatic coffee bean screening system using the developed classifier is manufactured and implemented for a local company.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on e-Business Engineering (ICEBE) |
Publisher | IEEE |
Pages | 271-275 |
ISBN (Electronic) | 9781665492447 |
ISBN (Print) | 9781665492454 |
DOIs | |
Publication status | Published - 14 Oct 2022 |
Keywords
- Deep Learning
- Coffee Beans Quality Screening
- classifier