Deep learning based coffee beans quality screening

Bing Shao, Yichen Hou, Nianqing Huang, Wei Wang, Xin Lu, Yanguo Jing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2022 IEEE International Conference on e-Business Engineering (ICEBE)
PublisherIEEE
Pages271-275
ISBN (Electronic)9781665492447
ISBN (Print)9781665492454
DOIs
Publication statusPublished - 14 Oct 2022

Keywords

  • Deep Learning
  • Coffee Beans Quality Screening
  • classifier

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