Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture

Authors

  • Edhy Poerwandono Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • M. Endang Taufik Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI:

https://doi.org/10.35870/jtik.v9i3.3826

Keywords:

Identification, HSV, GLCM, K-Nearest Neighbor

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.

Downloads

Download data is not yet available.

Author Biographies

  • Edhy Poerwandono, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika,

    Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.

  • M. Endang Taufik, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika,

    Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.

References

Acharya, T., & Ray, A. K. (2005). Image processing: principles and applications. John Wiley & Sons.

Amasino, R. (2010). Seasonal and developmental timing of flowering. The Plant Journal, 61(6), 1001-1013. https://doi.org/10.1242/dev.063511.

Huang, T. S., & Aizawa, K. (1993). Image processing: some challenging problems. Proceedings of the National Academy of Sciences, 90(21), 9766-9769.

Joly, A., Goëau, H., Bonnet, P., Bakić, V., Barbe, J., Selmi, S., ... & Barthélémy, D. (2014). Interactive plant identification based on social image data. Ecological Informatics, 23, 22-34.

Mouradov, A., Cremer, F., & Coupland, G. (2002). Control of flowering time: interacting pathways as a basis for diversity. The plant cell, 14(suppl_1), S111-S130.

Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons.

Pavlidis, T. (2012). Algorithms for graphics and image processing. Springer Science & Business Media.

Poerwandono, E., & Taufik, M. E. (2025). Identification of Flower Type Images Using KNN Algorithm With HSV Color Extraction and GLCM Texture. Router: Jurnal Teknik Informatika dan Terapan, 3(1), 01-14.

Sharma, A., Gupta, A., & Jaiswal, V. (2021). Solving image processing critical problems using machine learning. Machine Learning for Intelligent Multimedia Analytics: Techniques and Applications, 213-248.

Turner, B. M. (2009). Epigenetic responses to environmental change and their evolutionary implications. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), 3403-3418.

Vasconcelos, M. C., Greven, M., Winefield, C. S., Trought, M. C., & Raw, V. (2009). The flowering process of Vitis vinifera: a review. American journal of enology and viticulture, 60(4), 411-434.

Wood, A. (2023). Leaves and Flowers. BoD–Books on Demand.

Yuan, P., Li, W., Ren, S., & Xu, H. (2018). Recognition for flower type and variety of chrysanthemum with convolutional neural network. Transactions of the Chinese Society of Agricultural Engineering, 34(5), 152-158.

Downloads

Published

2025-07-01

Issue

Section

Computer & Communication Science

How to Cite

Poerwandono, E., & Taufik, M. E. (2025). Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(3), 1016-1023. https://doi.org/10.35870/jtik.v9i3.3826

Similar Articles

11-15 of 19

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)