Detection of citrus Huanglongbing based on image feature extraction and two-stage BPNN modeling

Abstract: Citrus Huanglongbing (HLB), which is spread by the citrus psyllid, is the most destructive disease of citrus industry. While no effective cure for the disease has been reported, detection and removal of infected trees can prevent spreading. Symptoms indicative of HLB can be present in both HLB-positive trees and HLB-negative trees, making identification of infected trees difficult. A detection method for citrus HLB based on image feature extraction and two-stage back propagation neural network (BPNN) modeling was investigated in this research. The identification method for eight different classes including healthy, HLB and non-HLB symptoms was studied. Thirty-four statistical features including color and texture were extracted for each leaf sample, following the two-stage BPNN to model and identify HLB-positive leaves from HLB-negative leaves. The discrimination accuracy can reach approximately 92% which shows that this method based on visual image processing can perform well in detecting citrus HLB.