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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Morphological Feature Extraction of Jabon’s Leaf Seedling Pathogen using Microscopic Image Melly Br Bangun; Yeni Herdiyeni; Elis Nina Herliyana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.2486

Abstract

This research aims to analyze morphological techniques for feature extraction of Jabon’s leaf seedling pathogen using digital microscopic image. The kinds of the pathogen were Curvularia sp., Colletotrichum sp., and Fusarium sp.. Pathogens or causes of disease were identified manually based on macroscopic and microscopic observation of morphological characters. Morphological characters describe the characteristics of shape, color and size of a pathogen structure. We focused on shape feature by using the morphological techniques to feature extraction. The morphology features extraction used were area, perimeter, convex area, convex perimeter, compactness, solidity, convexity, and roundness. The methodologies were acquisition, preprocessing, features extraction and data analysis for derivative features. With features extraction, we got the pattern that described each pathogen for pathogen identification. From the experimental result showed that compactness and roundness feature were able to differentiate each pathogen due to that the characteristics of each pathogen class were separated.
Leaf Morphological Feature Extraction of Digital Image Anthocephalus Cadamba Fuzy Yustika Manik; Yeni Herdiyeni; Elis Nina Herliyana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2675

Abstract

This research implemented an image feature extraction method using morphological techniques. The goal of this proccess is detecting objects that exist in the image. The image is converted into a grayscale image format. Then, grayscale image is processed with tresholding method to get initial segmentation. Furthermore, image from segmentation results are calculated using morphological methods to find the mapping of the original features into the new features. This process is done to get better class separation. Research conducted on two Antocephalus cadamba (Jabon) leaf diseased seedlings data set image that contained leaf spot disease and leaf blight. The results obtained morphological features such as rectangularity, roundness, compactness, solidity, convexity, elongation, and eccentricity able to represent the characteristic shape of the symptoms of the disease. All properties form the symptoms can be quantitatively explained by the features form. So it can be used to represent type of symptoms of two diseases in Antocephalus cadamba (Jabon).
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wavelet transform Felliks Feiters Tampinongkol; Yeni Herdiyeni; Elis Nina Herliyana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.10714

Abstract

Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%.
Co-Authors Abdul Muhyi ABDUL MUNIF Abdul Munif Abdurachman Syafiih Achmad Achmad . Achmad . Achmad . Achmad ; ACHMAD ACHMAD Achmad Achmad Achmad Lisdar Adisti Permatasari Putri Hartoyo Agus Setiawan Ai Rosah Aisah Akhir, Jumadil ANANG PRANOTO HIDAYAT Ananta Kusuma Amanda Andi Sukendro Andrea Ajeng Eirenne Kristianti Anisa Tri Harjanti Ardiansyah Putra Ariana Ariana Arief B. Witarto Arief Noor Rachmadiyanto Arif Ravi Wibowo Arzyana Sunkar Benyamin Dendang Darmono Taniwiryono Darmono Taniwiryono Darmono Taniwiryono Darmono Taniwiryono Deasy Putri Permatasari Dewi Sukma Dodi Nandika Dwierra Evvyernie Dyah LINGGA NP Erianto Indra Putra Eti Artiningsih Octaviani Fatin Hanifah Felliks Tampinongkol, Felliks Fifit Kulsum Fitri Kurniawati Fuzy Yustika Manik Gustan Pari Hanifah Nuryani Lioe Hayati Minarsih Hayati Minarsih Hidayatullah, Deden I. Sudirman Iga Dwi Syahrani Illa Anggraeni Irfan Jelata, Tedi Irfan Kemal Putra Irma Badarina Iskandar Z Siregar Isroi Isroi Ivan Permana Putra Kultsum, Fifit Kunio Tsunoda Labana Hutagalung Laila Fithri Maryam Libranika Dwi Miswati Liza Sakbani Lufthi Rusniarsyah, Lufthi Lul Godi, Rizal Lutfi Hanafi Melly Br Bangun Mira Febrianti Muhammad Alam Firmansyah Nabawiah, Safira Nifa Hanifa Noor Rachmadiyanto, Arief Nurulhaq, Muhammad Iqbal OSICA ASNO FERINA YURTI Ratna Jamilah Reny Meisetyani Reza Pamunca, Airres Rezeka Amalia Rizki Nugraheni Amaliasuci Rossy Nurhasanah Safira Nabawiah Santiyo Wibowo Sarah Diana Yulianti Shodiq Syifaudin, Ikhwan Silviana Arsyad Soetrisno Hadi Sri Listiyowati Sri Wahyuni, Devi Sri Wilarso Budi Surono Suryo Wiyono Syafitri Hidayati Tiara Antika Tjahja Muhandri Toto Toharmat TW DARMONO DARMONO Wartaka Wartaka ; Wasrin Syafii Yurico Bakhri Yusuf Sudo Hadi