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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) CAUCHY: Jurnal Matematika Murni dan Aplikasi Perfecting a Video Game with Game Metrics Lingua Jurnal Bahasa dan Sastra Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Karya Pendidikan Matematika Journal of ICT Research and Applications Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOIN (Jurnal Online Informatika) Jurnal Matematika: MANTIK JMPM: Jurnal Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Milang Journal of Mathematics and Its Applications Jurnal Matematika Integratif
Bib Paruhum Silalahi
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, IPB University, Bogor
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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Journal : JOIN (Jurnal Online Informatika)

 1 
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Co-Authors Agung Prajuhana Putra Agus Buono Agus Buono Agus Buono Agus Buono Amril Aman Danny Dimas Sulistio Elisabeth Sri Hendrastuti Fadhilah Syafria Fadhilah Syafria Fahren Bukhari Faisal Muhammad Fajar Setyawan Farahdila Sahara Farida Hanum Farida Hanum Franki Yusuf Bisilisin Haliza Suci Rachmadini Hidayatul Mayyani Hidayatul Mayyani Hidayatul Mayyani I Gede Arta Wibawa I Wayan Mangku I Wayan Mangku Inggih Permana Julio Adisantoso Karlisa Priandana Khoerul Fatihin Marisa Nurbaiti Mayyani, Hidayatul Mohamad Novanto Mu'min, Ulil Albab Muhammad Wiranadi Utama Mutia Atika Nabilah Diar Brigitha Ngakan Nyoman Kutha Krisnawijaya Nur Ahzan, Zulkaidah Nur Aliatiningtyas Nurul Fathiah Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Rafika Husnia Munfa'ati Silviana Eka Pertiwi Sri Nurdiati Sugi Guritman Sumanto, Sumanto Syeiva Nurul Desylvia T Murdani Saputra Ulil Albab Mu'min Yasin Yusuf