Texture Feature Extraction of Pathogen Microscopic Image Using  Discrete  Wavelet Transform

Hasan Basri

doi:10.34288/jri.v5i1.488

Hasan Basri ^(1*) Institut Teknologi dan Bisnis Bank Rakyat Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.34288/jri.v5i1.488

Keywords: DWT, Pathogen, Extraction Feature

Abstract

This study used a case study of Jabon leaves, and the pathogen is one of the causes of disease that attack the leaves of jabon, one of the leaf spots and leaf blight. Discovery of leaf spot disease in different pathogens and leaf blight. The pathogen was obtained from the leaf spot of Curvularia sp. 1 and Pestalotia sp., while the pathogen came from Curvularia sp. 2 and Botrytis sp. Identify the pathogen as soon as possible to minimize its effects. Improper handling can lead to increased virulence and resistance to the pathogen. Improper handling also can cause a disease outbreak (disease epidemic) in a region. This study is the first step in identifying the pathogens responsible for Jabon leaf disease. In this study, the Application of Koch's Postulates method to achieve the purification of pathogens and retrieve the microscopic pathogen image as the data acquisition stage. Furthermore, use of the segmentation stage to separate the object pathogen from the background, and one of the methods used is Otsu Thresholding. The extraction process of pathogen microscopic image using Discrete Wavelet Transform (DWT), DWT extraction results can be obtained using energy and entropy information.

Downloads

Download data is not yet available.

References

Agrios G. (2005). Plant Pathology (5th ed.). New York: Elsevier Academic.

Aisah, A. R. (2014). Identifikasi dan Patogenisitas Cendawan Penyebab Primer Penyakit Mati Pucuk pada Bibit Jabon ( Anthocephalus cadamba (Roxb.) Miq). IPB, Bogor.

Bangun, M. B., Herdiyeni, Y., & Herliyana, E. N. (2016). Morphological Feature Extraction of Jabon’s Leaf Seedling Pathogen using Microscopic Image. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(1). https://doi.org/10.12928/telkomnika.v14i1.2486

Hadi S. (2001). Masalah Dalam Perlindungan Hutan Terhadap Ganguan oleh Penyakit. In Patologi Hutan Perkembangannya di Indonesia. Bogor: Fakultas Kehutanan IPB.

Herliyana, E. N. (2013). Biodiversitas dan potensi cendawan di indonesia. Bogor: IPB Press.

Herliyana, E. N., Sakbani, L., Herdiyeni, Y., & Munif, A. (2020). Identifikasi Cendawan Patogen Penyebab Penyakit pada Daun Jabon Merah (Anthocephalus macrophyllus (Roxb.) Havil). Journal of Tropical Silviculture, 11(3), 154–162. https://doi.org/10.29244/j-siltrop.11.3.154-162

Larekeng, S. H., Qalbi, N., Rachmat, A., Iswanto, I., & Restu, M. (2022). Effect of gamma iradiated seeds of Jabon Merah (Neolamarckia macrophylla (Wall.) Bosser) to genetic diversity. IOP Conference Series: Earth and Environmental Science, 1115(1), 012027. https://doi.org/10.1088/1755-1315/1115/1/012027

Madhu, & Kumar, R. (2022). A hybrid feature extraction technique for content based medical image retrieval using segmentation and clustering techniques. Multimedia Tools and Applications, 81(6). https://doi.org/10.1007/s11042-022-11901-8

Naga Kiran D, & Kanchana V. (2019). Recognition of glaucoma using otsu segmentation method. International Journal of Research in Pharmaceutical Sciences, 10(3), 1988–1996. https://doi.org/10.26452/ijrps.v10i3.1407

Otsu, & N. (1996). A threshold selection method from gray-level histograms. IEEE Trans. on Systems, Man and Cybernetics, 9(1), 62–66. Retrieved from https://cw.fel.cvut.cz/b201/_media/courses/a6m33bio/otsu.pdf

Rafael C. Gonzalez, & Woods, R. E. (2008). Digital Image Processing. Hoboken, New Jersey: Prentice Hall.

Santosh, N. K., & Barpanda, S. S. (2020). 4. Wavelet applications in medical image processing. In Predictive Intelligence in Biomedical and Health Informatics (pp. 63–90). De Gruyter. https://doi.org/10.1515/9783110676129-004

Streets, R. B. (1972). The Diagnosis of Plant Diseases: A Field and Laboratory Manual Emphasizing the Most Practical Methods for Rapid Identification. Tucson, Arizona: University of Arizona Press.

Sudarsan, B., Ji, W., Adamchuk, V., & Biswas, A. (2018). Characterizing soil particle sizes using wavelet analysis of microscope images. Computers and Electronics in Agriculture, 148, 217–225. https://doi.org/10.1016/j.compag.2018.03.019

Tampinongkol, F. F., Herdiyeni, Y., & Herliyana, E. N. (2020). Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wavelet transform. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 740. https://doi.org/10.12928/telkomnika.v18i2.10714

Tan, C., Wang, Y., Zhou, X., Wang, Z., Zhang, L., & Liu, X. (2014). An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis. Journal of Sensors, 2014. https://doi.org/10.1155/2014/650891

Wang, S., Yang, X., Zhang, Y., Phillips, P., Yang, J., & Yuan, T.-F. (2015). Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine. Entropy, 17(12). https://doi.org/10.3390/e17106663

Warisno, & Dahana K. (2011). Peluang Investasi: Jabon Tanaman Kayu Masa Depan. Jakarta: Gramedia Pustaka Utama.

Widiyanto, S., Sukra, Y., Madenda, S., Wardani, D. T., & Wibowo, E. P. (2018). Texture Feature Extraction Based On GLCM and DWT for Beef Tenderness Classification. 2018 Third International Conference on Informatics and Computing (ICIC), 1–4. IEEE. https://doi.org/10.1109/IAC.2018.8780569