Field detection and classification of citrus Huanglongbing based on hyperspectral reflectance

Abstract Citrus Huanglongbing (HLB), also called citrus greening, is the most destructive disease in the citrus industry. Detecting the disease as early as possible and then eradicating infected roots can effectively control its spread. For the Shatangju mandarin cultivar, a non-destructive citrus HLB field detection method based on hyperspectral reflectance is proposed in this study. A characteristic band extraction method based on entropy distance and sequential backward selection is explored. Several machine learning algorithms (logistic regression, decision tree, support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning) were used to discriminate between disease groups: healthy, symptomatic HLB-infected, and asymptomatic HLB-infected, based on leaf reflectance. The results showed that the use of primary hyperspectral reflectance is very feasible for such classification. The band selection method proposed in this study provides an option for dimensionality reduction while still providing high classification accuracy. In three-group classification, the SVM learner achieved 90.8% accuracy, while in two-group classification (healthy vs symptomatic HLB leaves), the accuracy reached to 96%. The results also show that using only a few bands is insufficient for classification. In this study, 13 characteristic bands extracted by the proposed method provided the best performance.