Refining Content-Based Segmentation for Prediction of Coffee Bean Quality

Suhendro Yusuf Irianto

doi:10.24843/LKJITI.2023.v14.i02.p04

Suhendro Yusuf Irianto IIB Darmajaya

DOI: https://doi.org/10.24843/LKJITI.2023.v14.i02.p04

Abstract

Coffee has substantial economic value and is a key foreign exchange source for numerous nations, including Indonesia. Moreover, it is a primary livelihood for many of the country's farmers. Recently, there have been challenges in accurately predicting the quality of coffee beans, primarily due to time, inconsistency, and imprecision issues. Consequently, this study delves into the application of region-growing segmentation and content-based image retrieval (CBIR) techniques to enhance the prediction of coffee bean quality. The proposed hybrid approach, which combines region growing and CBIR methods, aims to improve the precision for forecasting cacao bean quality. Additionally, the research introduces an automated tool that employs these hybrid techniques for quality prediction. The study conducted experiments using a dataset of 400 premium and 400 low-quality coffee beans sourced from the University of Syiah Kuala in Indonesia. The results of the experiments demonstrate a commendable precision rate of 85.4%, showcasing significant improvement compared to certain previous studies.

Downloads

Download data is not yet available.

References

[1] E. D. Martauli, "Analysis Of Coffee Production In Indonesia," Journal of Agribusiness Sciences, vol. 01, no. 02, pp. 112–120, 2018.
[2] B. Raharjo and F. Agustini, “Metode Forward Chaining pada Sistem Pakar Penilaian Kualitas Biji Kopi Berbasis Web,” International Journal of Natural Science & Engineering (IJNSE), vol. 4, pp. 73–82, 2020.
[3] Y. Prastyaningsih, A. Noor, and A. Supriyanto, "Identifikasi Jenis Biji Kopi Menggunakan Ekstraksi Fitur Tekstur Berbasis Content Based Image Retrieval: Identification Types Of Coffee Beans Using Texture Feature Extraction Based On Content Based Image Retrieval," Computer Science and Informatics Journal, vol. 3, no. 2, pp. 105–116, 2020.
[4] J. Bai, D. Qin, P. Zheng, and L. Ma, “Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning,” ISPRS International Journal Geo-Information, vol. 12, no. 4, 2023, doi: 10.3390/ijgi12040169.
[5] J. Jin et al., "Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI," Frontier Oncology, vol. 13, no. March, pp. 1–10, 2023, doi: 10.3389/fonc.2023.1121594.
[6] M. B. Kuyu, M. Meshesha, and C. Diriba, "Grading Ethiopian Coffee Raw Quality Using Image Processing Techniques," Research Square, August 2022, doi: 10.35248/0970-1907.23.39.560-566.
[7] W. M. Kurniawan, “Penentuan Kualitas Biji Kopi Arabika Dengan Menggunakan Analytical Hierarchy Process ( Studi Kasus Pada Perkebunan Kopi Lereng Gunung Kelir Jambu Semarang )”, Jurnal Teknik Mesin, Elektro, dan Ilmu Komputer, Vol. 8, No. 2, Pp. 519–528, 2017.
[8] M. Olivya, E. Tungadi, and N. B. Rante, “Klasifikasi Kualitas Biji Kopi Ekspor Menggunakan Jaringan Saraf Tiruan Backpropagation,” Jurnal Sains dan Teknologi, vol. 3, no. 2, pp. 299–308, 2018.
[9] M. Merzougui and A. El Allaoui, "Region growing segmentation optimized by evolutionary approach and maximum entropy," Procedia Computer Science, vol. 151, pp. 1046–1051, 2019, doi: 10.1016/j.procs.2019.04.148.
[10] C. L. Kang, F. Wang, M. M. Zong, Y. Cheng, and T. N. Lu, "RESEARCH on IMPROVED REGION GROWING POINT CLOUD ALGORITHM," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Arch., vol. 42, no. 3/W10, pp. 153–157, 2020, doi: 10.5194/isprs-archives-XLII-3-W10-153-2020.
[11] H. Jiang, B. He, D. Fang, Z. Ma, B. Yang, and L. Zhang, "A region growing vessel segmentation algorithm based on spectrum information," Computational and Mathematical Methods in Medicine, vol. 2013, 2013, doi: 10.1155/2013/743870.
[12] R. Kaur, "Improving Efficiency of CBIR by using Color, Texture and Fusion Features with Bit Pattern," International Journal of Computer Applications, Vol. 178, no. 15, pp. 18–25, 2019.
[13] V. Lusiana et al., “Ekstraksi fitur tekstur menggunakan matriks glcm pada citra dengan variasi arah obyek 1,2,3,”, Seminar Nasional Multi Disiplin Ilmu Dan Call For Papers, Pp. 978–979, 2019.,
[14] S. H. Jadhav, "Content Based Image Retrieval System With Semantic Indexing and Recently Retrieved Image Library," International Journal of Computer Applications, vol. 4, no. 1, pp. 40–48, 2020.
[15] G. T. Situmorang, A. W. Widodo, and M. A. Rahman, “Penerapan Metode Gray Level Cooccurence Matrix ( GLCM ) untuk Ekstraksi Ciri pada Telapak Tangan”, Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer, vol. 3, no. 5, pp. 4710–4716, 2019.
[16] B. S. V, "Grey Level Co-Occurrence Matrices: Generalisation and Some New Features," International Journal of Computer Science, Engineering and Information Technology, vol. 2, no. 2, pp. 151–157, 2012, doi: 10.5121/ijcseit.2012.2213.
[17] S. Kusi-duah and O. Appiah, "Performance Evaluation of State-of-the-Art Texture Feature Extraction Techniques on Medical Imagery Tasks," Journal Current Research in Vaccines Vaccination, vol. 2, no. 1, pp. 1–23, 2023, doi: 10.33140/crvv.02.01.03.
[18] A. Susanto, “Matematika Citra Digital Untuk Ekstraksi Area Plat Nomor”, Jurnal Pseudocode, vol. VI, no. 1, pp. 49–57, 2019.
[19] F. M. Sarimole and A. Syaeful, "Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) and K-Nearest Neighbors (KNN)", Journal of Applied Engineering and Technological Science, vol. 4, no. 1, pp. 111–121, 2022, doi: 10.37385/jaets.v4i1.959.
[20] N. Rajlaxmi and L. S S, "Content Based Image Retrieval using Spectral Feature Extraction Methods," International Journal Electronic Communication Engineering, vol. 2, no. 4, pp. 10–13, 2015, doi: 10.14445/23488549/ijece-v2i4p104.
[21] S. Hamad, A. Iqbal, S. Naz, N. ul, M. Imran, and B. AlHaqbani, "Content-Based Image Retrieval Using Texture Color Shape and Region," International Journal Advanced. Computer Science Applications, vol. 7, no. 1, pp. 418–426, 2016, doi: 10.14569/ijacsa.2016.070156.
[22] C. Palai, P. K. Jena, S. R. Pattanaik, T. Panigrahi, and T. K. Mishra, "Content-based Image Retrieval using Encoder based RGB and Texture Feature Fusion," International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, pp. 245–254, 2023, doi: 10.14569/IJACSA.2023.0140328.
[23] A. Ghita and R. T. Ionescu, "Class Anchor Margin Loss for Content-Based Image Retrieval," 2023, [Online]. Available: http://arxiv.org/abs/2306.00630
[24] S. Kumar, M. K. Singh, and M. Mishra, "Efficient Deep Feature Based Semantic Image Retrieval," Neural Processing Letters, vol.55, no.3 January 2023, doi: 10.1007/s11063-022-11079-y.