Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)

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Semi-automatic Ground Truth Image Construction for Coffee Bean Defects Classification Based on SNI 01-2907-2008

Authors
Made Windu Antara Kesiman1, *, Ismail Sulaiman2
1Virtual, Vision, Image, and Pattern Research Group, Faculty of Engineering and Vocational, Universitas Pendidikan Ganesha, Bali, Indonesia
2Program Studi Teknologi Hasil Pertanian, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia
*Corresponding author. Email: antara.kesiman@undiksha.ac.id
Corresponding Author
Made Windu Antara Kesiman
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-122-7_43How to use a DOI?
Keywords
Dataset; Coffee bean; Defect; Classification
Abstract

In the coffee bean test procedure for determining the value of defects, physical separation of defective beans is carried out. This physical test is carried out using human senses or using assistive devices. It is necessary to have a system that can perform the identification and selection process of defective coffee beans automatically. To develop an automation system for determining the value of coffee bean defects based on SNI 01-2907-2008, a semi-automatic ground truth image construction is needed. In this study, a coffee bean storage device was designed, then a digital image is taken using a standard mobile phone camera. The digital image processing steps will be carried out. The results show that the proposed method is able to construct and to extract optimal number of the image samples of coffee bean for each type of defects. All extracted image samples of coffee bean defects will serve as a new dataset for the future automation system for determining the value of coffee bean defects based on SNI 01-2907-2008.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
Series
Advances in Biological Sciences Research
Publication Date
22 May 2023
ISBN
10.2991/978-94-6463-122-7_43
ISSN
2468-5747
DOI
10.2991/978-94-6463-122-7_43How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Made Windu Antara Kesiman
AU  - Ismail Sulaiman
PY  - 2023
DA  - 2023/05/22
TI  - Semi-automatic Ground Truth Image Construction for Coffee Bean Defects Classification Based on SNI 01-2907-2008
BT  - Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
PB  - Atlantis Press
SP  - 453
EP  - 463
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-122-7_43
DO  - 10.2991/978-94-6463-122-7_43
ID  - Kesiman2023
ER  -