| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02063 | |
| Number of page(s) | 8 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802063 | |
| Published online | 17 November 2023 | |
Identification Texture of Rice Varieties by Feature Extraction using GLCM
1 Doctoral Program of Information Systems, School of Postgraduate Studies, Diponegoro University, Semarang 50241, Central Java, Indonesia
2 Department of Information Technology, Faculty of Engineering, Darma Persada University, Jakarta, Indonesia
3 School of Postgraduate Studies, Diponegoro University, Semarang 50241, Central Java, Indonesia
4 Department of Physics, Faculty of Science and Mathematics, Diponegoro University, Semarang 50275, Central Java, Indonesia
* Corresponding author: aji_setiawan@ft.unsada.ac.id
Rice is one of the components of staple ingredients included in the issue of world food security in the Sustainable Development Goals (SDGs) program. A large number of varieties of rice types allows deviations in the field by mixing good rice varieties with other varieties to increase profits. This problem causes consumers to experience economic losses; on the other hand, distinguishing rice varieties is difficult to do directly only through eyesight. This study tries to make an initial approach to get the similarity value of each rice-type texture. This study discusses the extraction of texture features in 3 rice varieties, including organic rice, Mentik Wangi, Rojo Lele, and Basmati India. The feature extraction method used is the Gray Level Co-occurrence Matrix (GLCM) for assessing the texture of an image variety of rice and evaluated with PSNR and MAE.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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