Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach
DOI:
https://doi.org/10.30812/matrik.v23i3.3823Keywords:
Color Feature Extraction, Grape Variety Identificaton, Naive BayesAbstract
The problem addressed in this research is the lack of an efficient and accurate method for automatically identifying grape varieties. Accurate identification is crucial for quality control in the agricultural and food industries, impacting product labeling, pricing, and consumer trust. The aim of this research is to develop an automated system to classify green, black, and red grapes using digital image processing technology. This research method employs Naïve Bayes classification combined with color feature extraction. Testing was conducted under two scenarios: a database scenario with predefined grape image datasets and an out-of-database scenario with images resembling grape colors. Image processing includes resizing images to 200x200 pixels, Gamma Correction, Gaussian filtering, conversion to Lab* color space, K-Means Clustering for segmentation, followed by feature extraction and Naïve Bayes classification. The results of this research are that in the database scenario, the system achieved accuracies of 98.33% with an 80:20 data split and 98.89% with a 70:30 split. In the out-of-database scenario, accuracies were 96.67% with an 80:20 split and 97.78% with a 70:30 split. The conclusion of this research is the proposed method provides a reliable and efficient solution for automatic grape variety identification, benefiting quality control in agriculture and food industries.
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References
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Education in the Age of Artificial Intelligence,†in Lecture Notes in Electrical Engineering, R. N. Shaw, S. Das,
V. Piuri, and M. Bianchini, Eds., vol. 914. Singapore: Springer Nature Singapore, 2022, pp. 395–406, https://doi.org/10.1007/
978-981-19-2980-9 32.
[3] A. A. Bharate and M. S. Shirdhonkar, “Classification of Grape Leaves using KNN and SVM Classifiers,†Proceedings of the
4th International Conference on Computing Methodologies and Communication, ICCMC 2020, no. Iccmc, pp. 745–749, 2020,
https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000139.
[4] Pulung Nurtantio Andono and S. H. Nugraini, “Texture Feature Extraction in Grape Image Classification Using K-Nearest
Neighbor,†Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 768–775, 2022, https://doi.org/10.
29207/resti.v6i5.4137.
[5] L. Luo, W. Liu, Q. Lu, J. Wang, W. Wen, D. Yan, and Y. Tang, “Grape berry detection and size measurement based on edge
image processing and geometric morphology,†Machines, vol. 9, no. 10, 2021, https://doi.org/10.3390/machines9100233.
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selection,†International Journal of Computer Network and Information Security, vol. 12, no. 2, pp. 32–40, 2020, https://doi.
org/10.5815/ijcnis.2020.02.04.
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Healing of Covid-19 Patients in Indonesia,†3rd 2021 East Indonesia Conference on Computer and Information Technology,
EIConCIT 2021, pp. 41–44, 2021, https://doi.org/10.1109/EIConCIT50028.2021.9431845.
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recognition: a survey,†Multimedia Tools and Applications, vol. 82, no. 1, pp. 747–775, 2023, https://doi.org/10.1007/
s11042-022-13318-9.
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M. S. Khan, and M. E. Chowdhury, “Exploring the effect of image enhancement techniques on COVID-19 detection using chest
X-ray images,†Computers in Biology and Medicine, vol. 132, no. November 2020, 2021, https://doi.org/10.1016/j.compbiomed.
2021.104319.
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the 7th International Conference on Computing for Sustainable Global Development, INDIACom 2020, vol. 6, pp. 45–51, 2020,
https://doi.org/10.23919/INDIACom49435.2020.9083712.
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Multi-Color Space Embedding,†IEEE Transactions on Image Processing, vol. 30, pp. 4985–5000, 2021, https://doi.org/10.
1109/TIP.2021.3076367.
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Bioinformatics and Computational Biology, CIBCB 2020, 2020, https://doi.org/10.1109/CIBCB48159.2020.9277638.
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clustering,†International Journal of Computers and Applications, vol. 42, no. 7, pp. 649–654, 2020, https://doi.org/10.1080/
1206212X.2018.1521090.
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Citra Menggunakan SVM dan KNN,†vol. 4, no. 1, pp. 28–36, 2021, https://doi.org/10.25273/research.v4i1.6687.
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A review. Multimedia Tools and Applications, 2022, vol. 81, no. 12, https://doi.org/10.1007/s11042-022-12412-2.
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Journal of Modern Education and Computer Science, vol. 13, no. 3, pp. 57–67, 2021, https://doi.org/10.5815/IJMECS.
2021.03.05.
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mining,†IOP Conf. Series: Materials Science and Engineering, 2020, https://doi.org/10.1088/1757-899X/725/1/012106.
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Published
2024-07-12
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Section
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How to Cite
[1]
P. Jafar, D. Indra, and F. Umar, “Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach”, MATRIK, vol. 23, no. 3, pp. 677–689, Jul. 2024, doi: 10.30812/matrik.v23i3.3823.
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