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

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 such as citrus canker, black spot, devastating Huanglongbing (HLB) disease. HLB, caused by gram-negative proteobacteria strains, severely impacts orchards globally, resulting in economic losses. Early detection HLB-infected plants crucial effective disease management. Traditional approaches rely on expert knowledge time-consuming laboratory tests, hindering rapid detection. This study explores an alternative method using BEMD algorithm texture feature extraction SVM improve HLB diagnosis. The decomposes leaf images into Intrinsic Mode Functions (IMFs) a residue component. Classification experiments were conducted IMF 1, 2, features. component provided most outstanding level accuracy, reaching 77% two classes, 72% three types, 61% four classes. In categories, 1 performed at accuracy rate, other areas, it 51% making competitive. 2 demonstrated lower ranging from 43% classes 57% categories. findings highlight significance image component, outperforming features accuracy. coupled with presents promising approach diagnosis, surpassing performance previous studies GLCM-SVM techniques.