Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis


  • Sumanto 1) Department of Computer Science, IPB University, Indonesia 2) Department of Information System, Universitas Bina Sarana Informatika, Indonesia, Indonesia
  • Agus Buono Department of Computer Science, IPB University, Indonesia, Indonesia
  • Karlisa Priandana Department of Computer Science, IPB University, Indonesia, Indonesia
  • Bib Paruhum Silalahi Department of Mathematics, IPB University, Indonesia, Indonesia
  • Elisabeth Sri Hendrastuti Department of Plant Protection, IPB University, Indonesia, Indonesia




BEMD, Citrus Greening, Huanglongbing, Residual Image


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.


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