Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level co-occurrence matrix dan lab color moments

Authors

  • Evy Kamilah Ratnasari Teknik Informatika - Universitas Dr. Soetomo Surabaya
  • Raden Venantius Hari Ginardi Teknik Informatika - Institut Teknologi Sepuluh Nopember
  • Chastine Fatichah Teknik Informatika - Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.26594/register.v3i1.575

Keywords:

color moments, GLCM, segmentation, spot disease, sugarcane leaf image, citra daun tebu, penyakit noda, segmentasi

Abstract

 

Penyakit noda pada daun tanaman tebu menampakkan gejala berupa lesi atau bercak. Lesi tersebut menghambat proses fotosintesis daun dan dapat mengakibatkan menurunnya produksi gula. Oleh karena itu, dalam meningkatkan kualitas produksi gula dibutuhkan diagnosa dini untuk mengambil keputusan penanganan penyakit yang cepat dan tepat, sehingga dapat meminimalisir kerusakan daun yang signifikan akibat penyebaran penyakit tersebut. Sayangnya keterbatasan keberadaan ahli penyakit tanaman tebu yang berpotensi dalam mendiagnosa penyakit noda tidak dapat mengatasi hal tersebut. Penelitian ini mengusulkan diagnosa penyakit noda tanaman tebu menggunakan metode pemrosesan citra berdasarkan fitur tekstur Segmentation-based Gray Level Co-Occurrence Texture (SGLCM) dan LAB color moments. Metode yang diajukan terdiri dari ekstraksi ciri warna pada citra masukan yang akan menghasilkan 12 fitur warna dan ekstraksi ciri tekstur pada citra masukan yang tersegmentasi dan menghasilkan 24 fitur tekstur, kemudian gabungan fitur warna dan tekstur tersebut digunakan sebagai masukan klasifikasi k-Nearest Neighbor (kNN) untuk mengenali jenis penyakit noda pada citra daun tanaman tebu. Jenis penyakit noda terdiri dari noda cincin, noda karat, dan noda kuning yang memiliki karakteristik berbeda. Klasifikasi penyakit noda pada tanaman tebu  menggunakan metode tersebut dapat menghasilkan akurasi tertinggi 93%.

 

 

 

The sugarcane spot disease attack the sugarcane with appear as spots on the leaves, so this spots prevent the vital process of photosynthesis to take place and caused sugar production losses. Early diagnosis of this spot disease can improve the quality of sugar production. The diagnosis result can be used as decision reference to control the disease fast and accurately to minimize attack severe that can caused significant damage. Unfortunately, experts who are able to identify the diseases are often unavailable. This research attempted to identify the three sugarcane spot diseases (ring spot, rust spot, and yellow spot) using Segmentation-based Gray Level Co-Occurrence Texture (SGLCM) and LAB color moments. The SGLCM obtain 24 texture features of segmented image and color moments obtain 12 color features. This method achieved at least 93% accuracy when identifying the diseases using kNN classifier.

References

Asfarian, A., Herdiyeni, Y., Rauf, A., & Mutaqin, K. H. (2013). Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. Computer, Control, Informatics and Its Applications (IC3INA), 2013 International Conference on (hal. 77-81). Jakarta: IEEE.

Bashish, D. A., Braik, M., & Ahmad, S. B. (2010). A framework for detection and classification of plant leaf and stem diseases. Signal and Image Processing (ICSIP), 2010 International Conference on (hal. 113-118). Chennai: IEEE.

Busin, L., Vandenbroucke, N., & Macaire, L. (2008). Color spaces and image segmentation. Advances in Imaging and Electron Physics, 151, 65-168.

Chaudhary, P., Chaudhari, A. K., Cheeran, A. N., & Godara, S. (2012). Color transform based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications, 3(6), 65-70. https://pdfs.semanticscholar.org/aa8d/9fa4ee083eb927e42485c071942109c12f09.pdf.

Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27.

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610-621.

Huang, K.-Y. (2007). Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in Agriculture, 57(1), 3–11.

Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (2013). Leaf classification using shape, color, and texture features. International Journal of Computer Trends and Technology, 225-230. https://arxiv.org/abs/1401.4447.

Kusuma, A. P., & Darmanto, D. (2016). Pengenalan angka pada sistem operasi android dengan menggunakan metode template matching. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 2(2), 68-78.

Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285–295.

Meunkaewjinda, A., Kumsawat, P., Attakitmongcol, K., & Srikaew, A. (2008). Grape leaf disease detection from color imagery using hybrid intelligent system. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on (hal. 513-516). Krabi: IEEE.

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.

Rathod, A. N., Tanawal, B., & Shah, V. (2013). Image processing techniques for detection of leaf disease. International Journal of Advanced Research in Computer Science and Software Engineering, 3(11), 397-399. http://www.academia.edu/12118677/Image_Processing_Techniques_for_Detection_of_Leaf_Disease.

Ratnasari, E. K., Ginardi, R. V., & Fatichah, C. (2014). Pengenalan penyakit noda pada citra daun tebu berdasarkan ciri tekstur fractal dimension co-occurrence matrix dan L*a*b* color moments. JUTI, 12(2), 27– 36.

Rott, P. (2000). A guide to sugarcane diseases. Paris: Quae. http://www.quae.com/en/r122-guide-des-maladies-de-la-canne-a-sucre.html.

Sa’diyah, N., & Aeny, T. N. (2012). Keragaman dan heritabilitas ketahanan tebu populasi F1 terhadap penyakit bercak kuning di PT. Gunung Madu Plantations Lampung. Jurnal Hama dan Penyakit Tumbuhan Tropika, 12(1), 71-77. http://jhpttropika.fp.unila.ac.id/index.php/jhpttropika/article/view/118.

Sungkur, R. K., Baichoo, S., & Poligadu, A. (2013). An automated system to recognise fungi-caused diseases on sugarcane leaves. Proceedings of Global Engineering, Science and Technology Conference. Bencoolen, Singapura: Global Institute of Science & Technology. https://gistworldconpro.com/uploads/6/28/1424167428_328-roopseh.pdf.

Vibhute, A., & Bodhe, S. K. (2012). Applications of image processing in agriculture: A survey. International Journal of Computer Applications, 52(2), 34-40.

Downloads

Published

2017-01-01

How to Cite

[1]
E. K. Ratnasari, R. V. H. Ginardi, and C. Fatichah, “Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level co-occurrence matrix dan lab color moments”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 3, no. 1, pp. 1–10, Jan. 2017.

Issue

Vol. 3 No. 1 (2017): January

Section

Article

License


Please find the rights and licenses in Register: Jurnal Ilmiah Teknologi Sistem Informasi. By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.

1. License

The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

2. Author(s)' Warranties

The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).

3. User/Public Rights

Register's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Register permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Register on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.

4. Rights of Authors

Authors retain all their rights to the published works, such as (but not limited to) the following rights;

Copyright and other proprietary rights relating to the article, such as patent rights,
The right to use the substance of the article in own future works, including lectures and books,
The right to reproduce the article for own purposes,
The right to self-archive the article (please read out deposit policy),
The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Register: Jurnal Ilmiah Teknologi Sistem Informasi).
5. Co-Authorship

If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Register will not be held liable for anything that may arise due to the author(s) internal dispute. Register will only communicate with the corresponding author.

6. Royalties

Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Register entitles the author(s) to no royalties or other fees.

7. Miscellaneous

Register will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Register's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.